The best Bluetooth trackers for 2020

Struggle to keep track of important items? A Bluetooth tracker could change your life (and save your sanity).

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On the Week of the Election, Social Media Must Go Dark

CEOs should voluntarily shut down their platforms to help stanch the spread of misinformation—and protect democracy.

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Scientists Discover the First Room-Temperature Superconductor

Physicists finally achieved the long-sought goal, but there’s a catch: Their compound requires crushing pressures to keep from falling apart.

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4 Ways to Sell or Trade In Your Old iPhone

If you plan to upgrade to Apple's new models, don't forget to cash in on your old one.

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I Didn’t Want to Love Zooming on My Facebook Portal—but I Do

Stop trying to duct-tape a ring light to your laptop, and get yourself a dedicated videophone.

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How to Clean Up Your Digital History

There are plenty of reasons to declutter your online traces. Here's how to tidy up.

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The Unsinkable Maddie Stone, Google's Bug-Hunting Badass

The Project Zero reverse engineer shuts down some of the world's most dangerous exploits—along with antiquated hacker stereotypes.

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With Vacation Rentals Empty, European Cities See a Chance to Reclaim Housing

Platforms like Airbnb have been criticized for raising living costs in popular cities. Now, officials are trying to move properties aimed at tourists to long-term leases.

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Weibo users describe how political discussions on the platform have become even more muted amid pervasive censorship, as some resort to using burner accounts (Shen Lu/Rest of World)

Shen Lu / Rest of World:
Weibo users describe how political discussions on the platform have become even more muted amid pervasive censorship, as some resort to using burner accounts  —  Thousands of Weibo accounts have been deleted as China's government cracks down on free speech.

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Google Pixel 5's wimpy camera is driving me to the iPhone 12 - CNET

Commentary: For this photo enthusiast, the Pixel 5's middling camera hardware has undercut Google's advanced image processing.

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Apple Watch Series 6 vs. Fitbit Sense: Top smartwatches go head to head - CNET

We compare the flagship Apple and Fitbit smartwatches to help you decide between the two.

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iPhone 12 vs. Pro and Pro Max: The features that might convince you to go Pro - CNET

Do you really need an iPhone 12 "Pro" model or is the regular iPhone 12 enough?

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Samsung Group titan Lee Kun-hee dies aged 78

Mr Lee helped to grow his father's small trading business into a global industrial powerhouse.

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Apple, Google and a Deal That Controls the Internet

In a landmark antitrust complaint, the Justice Department is targeting a secretive partnership that is worth billions of dollars to both companies.

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Profile of Palantir CEO Alex Karp and the controversies around Palantir's trustworthiness; Karp claims his progressivism offsets Thiel's relationship with Trump (Michael Steinberger/New York Times)

Michael Steinberger / New York Times:
Profile of Palantir CEO Alex Karp and the controversies around Palantir's trustworthiness; Karp claims his progressivism offsets Thiel's relationship with Trump  —  The tech giant helps governments and law enforcement decipher vast amounts of data — to mysterious and, some say, dangerous ends.

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Zest AI raises $15M for its AI-powered tech that it claims can help financial institutions reduce bias in loan portfolios, bringing its total VC funding to $87M (Kyle Wiggers/VentureBeat)

Kyle Wiggers / VentureBeat:
Zest AI raises $15M for its AI-powered tech that it claims can help financial institutions reduce bias in loan portfolios, bringing its total VC funding to $87M  —  Zest AI, a company developing AI-powered loan decisioning products, today closed a $15 million funding round led by Insight Partners.

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The best smart ovens of 2020: Amazon, June, Tovala and more - CNET

Smart ovens for your countertop are a thing now, and there are several good options. From remote cooking to food recognition, there's a whole new world of cooking out there.

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FinLocker, whose software helps consumers expedite the mortgage approval process, raises $19.8M Series A and announces a commercial agreement with TransUnion (David Nicklaus/St. Louis Post-Dispatch)

David Nicklaus / St. Louis Post-Dispatch:
FinLocker, whose software helps consumers expedite the mortgage approval process, raises $19.8M Series A and announces a commercial agreement with TransUnion  —  FinLocker, a Clayton-based financial technology company, has raised $19.8 million in venture capital and reached a commercial agreement with a major credit bureau.

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Samsung Chairman Lee, the man who made the company an electronics giant, dies at 78 - CNET

Kun-Hee Lee has been hospitalized for years, but Samsung didn't specify what caused his death.

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Zoom deleted events planned for Oct. 23 on alleged censorship by the company after Zoom canceled an SFSU talk in Sept.; Zoom says the events violated its TOS (Jane Lytvynenko/BuzzFeed News)

Jane Lytvynenko / BuzzFeed News:
Zoom deleted events planned for Oct. 23 on alleged censorship by the company after Zoom canceled an SFSU talk in Sept.; Zoom says the events violated its TOS  —  Zoom shut down a series of events meant to discuss what organizers called “censorship” by the company.

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Lee Kun-hee, who was chairman and chief executive of Samsung Electronics from 1998 to 2008 and its chairman since 2010, has died at age 78 (Raymond Zhong/New York Times)

Raymond Zhong / New York Times:
Lee Kun-hee, who was chairman and chief executive of Samsung Electronics from 1998 to 2008 and its chairman since 2010, has died at age 78  —  Mr. Lee was convicted — and pardoned — twice for white-collar crimes, in a sign of the ills in South Korea's relationship with its business dynasties.

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Best life insurance companies for 2020 - CNET

Everything you need to know about the best type of policies and the insurance companies that sell them.

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Climate change: Technology no silver bullet, experts tell PM

Boris Johnson's techno-optimism ignores the need for big societal changes, experts warn.

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Fitbit CEO James Park says its app reached 500,000 paid subscribers this year and talks about expectations for life under Google, competing with Apple, more (John D. Stoll/Wall Street Journal)

John D. Stoll / Wall Street Journal:
Fitbit CEO James Park says its app reached 500,000 paid subscribers this year and talks about expectations for life under Google, competing with Apple, more  —  Fitbit CEO James Park discusses the company's new products and competition as it prepares to close its acquisition by Google

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Why AI live fact-checked the 2020 US presidential debates

The software should be able to speed up the time in takes to separate truth from fiction.

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2020 AFL Grand Final: How to watch Tigers vs. Cats, US start time, cable, streaming - CNET

Australia's Super Bowl is on this Saturday.

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iPhone 12 teardowns offer inside peek of Apple's latest phones - CNET

The video shows how much thinner the iPhone 12's display is, as well as the MagSafe technology.

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First murder hornet nest found in US is about to be destroyed - CNET

The elusive Asian giant hornets' nest in Washington state won't be there for long.

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Trump’s Claims About Hunter Biden Send Online Activity Soaring

Google searches and Facebook posts about Joseph R. Biden Jr.’s son rose after President Trump’s misleading comments at Thursday’s debate.

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DoorDash is investing in a restaurant, Oakland's Burma Bites, for the first time, says the investment will help it build a more permanent delivery model (Justin Phillips/San Francisco Chronicle)

Justin Phillips / San Francisco Chronicle:
DoorDash is investing in a restaurant, Oakland's Burma Bites, for the first time, says the investment will help it build a more permanent delivery model  —  For the first time in the company's history, the food delivery app DoorDash has co-invested in a restaurant.

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Trump’s Claims About Hunter Biden Sends Online Activity Soaring

Google searches and Facebook posts about Joseph R. Biden Jr.’s son rose after President Trump’s misleading comments at Thursday’s debate.

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NASA steals bits of asteroid Bennu: What you need to know about the mission - CNET

The Osiris-Rex spacecraft swiped souvenirs from Bennu on Tuesday to bring home to Earth.

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Ford family members auctioning Bronco First Edition for charity - Roadshow

It's a sweet spec, but the price is more than double MSRP.

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Stock up for fall projects with these deeply discounted Skil power tool combos - CNET

Whether you need a drill, laser tape measure and level, circular saw or impact driver, there's something here for you.

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Among Us hack promotes 'Trump 2020,' forcing developer to issue patch - CNET

The hack also threatened to damage phones if people didn't subscribe to a streaming channel.

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Facebook, Twitter CEOs to testify about allegations of anti-conservative bias - CNET

The hearing before the Senate Judiciary Committee will also focus on how the social networks handled the 2020 US elections.

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Uber drivers sue, say company 'coerced' them to support Prop 22 - CNET

A lawsuit alleges Uber used a "constant barrage" of "propaganda," "old-school exploitation" and "scare tactics" to get drivers to back its ballot measure campaign.

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Best dad ever connects Mario Kart Live: Home Circuit to full-motion sim rig - Roadshow

This engineer got Mario Kart Live: Home Circuit to work with his full-motion racing simulator.

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Justice League Snyder Cut reportedly adds Joe Manganiello's Deathstroke - CNET

Actors Ben Affleck, Henry Cavill, Gal Gadot, Ray Fisher and Jared Leto will also be in Zack Snyder's new version of the Justice League movie.

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The Nintendo Switch Lite is in stock right now - CNET

With production promising to return to normal soon, here's the status of the $200 version of Nintendo's game console.

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Disney struggles with reopening global theme parks as Disneyland finally gets California rules - CNET

The company is laying off 28,000 US employees as Disneyland stays shut and Disney World attendance remains capped.

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The Nintendo Switch is actually in stock, including the Animal Crossing special edition - CNET

Here are the latest details on where you can buy the red-hot $300 console online.

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Patreon will remove accounts from 'QAnon-dedicated creators' - CNET

The company updated its policies to address the promotion of QAnon-related disinformation.

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Save up to $30 filling in your Wi-Fi dead zones during this Rock Space router sale - CNET

Choose a 3-piece whole-home mesh router or one of two Wi-Fi range extenders.

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Rokt, which helps e-commerce companies make personalized marketing offers during and after transactions, raises $80M Series D, at a valuation of $450M+ (Sahil Patel/Wall Street Journal)

Sahil Patel / Wall Street Journal:
Rokt, which helps e-commerce companies make personalized marketing offers during and after transactions, raises $80M Series D, at a valuation of $450M+  —  Rokt tools let companies make personalized marketing offers during and after transactions  —  Rokt Pte Ltd., an eight-year-old e …

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Mark Zuckerberg and Jack Dorsey will testify before the Senate Judiciary Committee on Nov. 17 on their platforms' handling of NY Post story and the election (Jay Peters/The Verge)

Jay Peters / The Verge:
Mark Zuckerberg and Jack Dorsey will testify before the Senate Judiciary Committee on Nov. 17 on their platforms' handling of NY Post story and the election  —  Facebook CEO Mark Zuckerberg and Twitter CEO Jack Dorsey will testify before the Senate Judiciary Committee on November 17th.

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First NASA Osiris-Rex images show incredible touchdown on asteroid Bennu - CNET

NASA releases the first batch of images from Osiris-Rex, showing an explosive impact on the potentially hazardous asteroid.

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Lyft says riders will have the option to pay for and split fares using Venmo in the coming weeks (Matt Burns/TechCrunch)

Matt Burns / TechCrunch:
Lyft says riders will have the option to pay for and split fares using Venmo in the coming weeks  —  Lyft riders will soon have the option for paying and splitting fares using Venmo, the company said in a blog posting this morning.  Venmo joins Lyft's other payment methods of PayPal, credit cards, debit cards, Lyft Cash and more.

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A look at Google's Next Billion Users team, tasked with building products and apps for people who have yet to use the internet, often in developing markets (Katie Deighton/Wall Street Journal)

Katie Deighton / Wall Street Journal:
A look at Google's Next Billion Users team, tasked with building products and apps for people who have yet to use the internet, often in developing markets  —  The company's Go apps aim to teach new internet users the web's visual language  —  Alphabet Inc.'s Google in March unveiled …

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Executive Interview: Brian Gattoni, CTO, Cybersecurity & Infrastructure Security Agency

Understanding and Advising on Cyber and Physical Risks to the Nation’s Critical Infrastructure 

Brian Gattoni, CTO, Cybersecurity & Infrastructure Security Agency

Brian R. Gattoni is the Chief Technology Officer for the Cybersecurity and Infrastructure Security Agency (CISA) of the Department of Homeland Security. CISA is the nation’s risk advisor, working with partners to defend against today’s threats and collaborating to build a secure and resilient infrastructure for the future. Gattoni sets the technical vision and strategic alignment of CISA data and mission services. Previously, he was the Chief of Mission Engineering & Technology, developing analytic techniques and new approaches to increase the value of DHS cyber mission capabilities. Prior to joining DHS in 2010, Gattoni served in various positions at the Defense Information Systems Agency and the United States Army Test & Evaluation Command. He holds a Master of Science Degree in Cyber Systems & Operations from the Naval Postgraduate School in Monterey, California, and is a Certified Information Systems Security Professional (CISSP).  

AI Trends: What is the technical vision for CISA to manage risk to federal networks and critical infrastructure? 

Brian Gattoni: Our technology vision is built in support of our overall strategy. We are the nation’s risk advisor. It’s our job to stay abreast of incoming threats and opportunities for general risk to the nation. Our efforts are to understand and advise on cyber and physical risks to the nation’s critical infrastructure.  

It’s all about bringing in the data, understanding what decisions need to be made and can be made from the data, and what insights are useful to our stakeholders. The potential of AI and machine learning is to expand on operational insights with additional data sets to make better use of the information we have.  

What are the most prominent threats? 

The Cybersecurity and Infrastructure Security Agency (CISA) of the Department of Homeland Security is the Nation’s risk advisor.

The sources of threats we frequently discuss are the adversarial actions of nation-state actors and those aligned with nation-state actors and their interests, in disrupting national critical functions here in the U.S. Just in the past month, we’ve seen increased activity from elements supporting what we refer to in the government as Hidden Cobra [malicious cyber activity by the North Korean government]. We’ve issued joint alerts with our partners overseas and the FBI and the DoD, highlighting activity associated with Chinese actors. On CISA.gov people can find CISA Insights, which are documents that provide background information on particular cyber threats and the vulnerabilities they exploit, as well as a ready-made set of mitigation activities that non-federal partners can implement.   

What role does AI play in the plan? 

Artificial intelligence has a great role to play in the support of the decisions we make as an agency. Fundamentally, AI is going to allow us to apply our decision processes to a scale of data that humans just cannot keep up with. And that’s especially prevalent in the cyber mission. We remain cognizant of how we make decisions in the first place and target artificial intelligence and machine learning algorithms that augment and support that decision-making process. We’ll be able to use AI to provide operational insights at a greater scale or across a greater breadth of our mission space.  

How far along are you in the implementation of AI at the CISA? 

Implementing AI is not as simple as putting in a new business intelligence tool or putting in a new email capability. Really augmenting your current operations with artificial intelligence is a mix of the culture change, for humans to understand how the AI is supposed to augment their operations. It is a technology change, to make sure you have the scalable compute and the right tools in place to do the math you’re talking about implementing. And it’s a process change. We want to deliver artificial intelligence algorithms that augment our operators’ decisions as a support mechanism.  

Where we are in the implementation is closer to understanding those three things. We’re working with partners in federally funded research and development centers, national labs and the department’s own Science and Technology Data Analytics Tech Center to develop capability in this area. We’ve developed an analytics meta-process which helps us systemize the way we take in data and puts us in a position to apply artificial intelligence to expand our use of that data.  

Do you have any interesting examples of how AI is being applied in CISA and the federal government today? Or what you are working toward, if that’s more appropriate. 

I have a recent use case. We’ve been working with some partners over the past couple of months to apply AI to a humanitarian assistance and disaster relief type of mission. So, within CISA, we also have responsibilities for critical infrastructure. During hurricane season, we always have a role to play in helping advise what the potential impacts are to critical infrastructure sites in the affected path of a hurricane.  

We prepared to conduct an experiment leveraging AI algorithms and overhead imagery to figure out if we could analyze the data from a National Oceanic and Atmospheric Administration flight over the affected area. We compared that imagery with the base imagery from Google Earth or ArcGIS and used AI to identify any affected critical infrastructure. We could see the extent to which certain assets, such as oil refineries, were physically flooded. We could make an assessment as to whether they hit a threshold of damage that would warrant additional scrutiny, or we didn’t have to apply resources because their resilience was intact, and their functions could continue.   

That is a nice use case, a simple example of letting a computer do the comparisons and make a recommendation to our human operators. We found that it was very good at telling us which critical infrastructure sites did not need any additional intervention. To use a needle in a haystack analogy, one of the useful things AI can help us do is blow hay off the stack in pursuit of the needle. And that’s a win also. The experiment was very promising in that sense.  

How does CISA work with private industry, and do you have any examples of that?  

We have an entire division dedicated to stakeholder engagement. Private industry owns over 80% of the critical infrastructure in the nation. So CISA sits at the intersection of the private sector and the government to share information, to ensure we have resilience in place for both the government entities and the private entities, in the pursuit of resilience for those national critical functions. Over the past year we’ve defined a set of 55 functions that are critical for the nation.  

When we work with private industry in those areas we try to share the best insights and make decisions to ensure those function areas will continue unabated in the face of a physical or cyber threat. 

Cloud computing is growing rapidly. We see different strategies, including using multiple vendors of the public cloud, and a mix of private and public cloud in a hybrid strategy. What do you see is the best approach for the federal government? 

In my experience the best approach is to provide guidance to the CIO’s and CISO’s across the federal government and allow them the flexibility to make risk-based determinations on their own computing infrastructure as opposed to a one-size-fits-all approach.   

We issue a series of use cases that describe—at a very high level—a reference architecture about a type of cloud implementation and where security controls should be implemented, and where telemetry and instrumentation should be applied. You have departments and agencies that have a very forward-facing public citizen services portfolio, which means access to information, is one of their primary responsibilities. Public clouds and ease of access are most appropriate for those. And then there are agencies with more sensitive missions. Those have critical high value data assets that need to be protected in a specific way. Giving each the guidance they need to handle all of their use cases is what we’re focused on here. 

I wanted to talk a little bit about job roles. How are you defining the job roles around AI in CISA, as in data scientists, data engineers, and other important job titles and new job titles?  

I could spend the remainder of our time on this concept of job roles for artificial intelligence; it’s a favorite topic for me. I am a big proponent of the discipline of data science being a team sport. We currently have our engineers and our analysts and our operators. And the roles and disciplines around data science and data engineers have been morphing out of an additional duty on analysts and engineers into its own sub sector, its own discipline. We’re looking at a cadre of data professionals that serve almost as a logistics function to our operators who are doing the mission-level analysis. If you treat data as an asset that has to be moved and prepared and cleaned and readied, all terms in the data science and data engineering world now, you start to realize that it requires logistics functions similar to any other asset that has to be moved. 

If you get professionals dedicated to that end, you will be able to scale to the data problems you have without overburdening your current engineers who are building the compute platforms, or your current mission analysts who are trying to interpret the data and apply the insights to your stakeholders. You will have more team members moving data to the right places, making data-driven decisions. 

Are you able to hire the help you need to do the job? Are you able to find qualified people? Where are the gaps? 

As the domain continues to mature, as we understand more about the different roles, we begin to see gaps—education programs and training programs that need to be developed. I think maybe three, five years ago, you would see certificates from higher education in data science. Now we’re starting to see full-fledged degrees as concentrations out of computer science or mathematics. Those graduates are the pipeline to help us fill the gaps we currently have. So as far as our current problems, there’s never enough people. It’s always hard to get the good ones and then keep them because the competition is so high. 

Here at CISA, we continue to invest not only in our own folks that are re-training, but in the development of a cyber education and training group, which is looking at the partnerships with academia to help shore up that pipeline. It continually improves. 

Do you have a message for high school or college students interested in pursuing a career in AI, either in the government or in business, as to what they should study? 

Yes and it’s similar to the message I give to the high schoolers that live in my house. That is, don’t give up on math so easily. Math and science, the STEM subjects, have foundational skills that may be applicable to your future career. That is not to discount the diversity and variety of thought processes that come from other disciplines. I tell my kids they need the mathematical foundation to be able to apply the thought processes you learn from studying music or studying art or studying literature. And the different ways that those disciplines help you make connections. But have the mathematical foundation to represent those connections to a computer.   

One of the fallacies around machine learning is that it will just learn [by itself]. That’s not true. You have to be able to teach it, and you can only talk to computers with math, at the base level.  

So if you have the mathematical skills to relay your complicated human thought processes to the computer, and now it can replicate those patterns and identify what you’re asking it to do, you will have success in this field. But if you give up on the math part too early—it’s a progressive discipline—if you give up on algebra two and then come back years later and jump straight into calculus, success is going to be difficult, but not impossible. 

You sound like a math teacher.  

A simpler way to say it is: if you say no to math now, it’s harder to say yes later. But if you say yes now, you can always say no later, if data science ends up not being your thing.  

Are there any incentives for young people, let’s say a student just out of college, to go to work for the government? Is there any kind of loan forgiveness for instance?  

We have a variety of programs. The one that I really like, that I have had a lot of success with as a hiring manager in the federal government, especially here at DHS over the past 10 years, is a program called Scholarship for Service. It’s a CyberCorps program where interested students, who pass the process to be accepted can get a degree in exchange for some service time. It used to be two years; it might be more now, but they owe some time and service to the federal government after the completion of their degree. 

I have seen many successful candidates come out of that program and go on to fantastic careers, contributing in cyberspace all over. I have interns that I hired nine years ago that are now senior leaders in this organization or have departed for private industry and are making their difference out there. It’s a fantastic program for young folks to know about.  

What advice do you have for other government agencies just getting started in pursuing AI to help them meet their goals? 

My advice for my peers and partners and anybody who’s willing to listen to it is, when you’re pursuing AI, be very specific about what it can do for you.   

I go back to the decisions you make, what people are counting on you to do. You bear some responsibility to know how you make those decisions if you’re really going to leverage AI and machine learning to make decisions faster or better or some other quality of goodness. The speed at which you make decisions will go both ways. You have to identify your benefit of that decision being made if it’s positive and define your regret if that decision is made and it’s negative. And then do yourself a simple HIGH-LOW matrix; the quadrant of high-benefit, low-regret decisions is the target. Those are ones that I would like to automate as much as possible. And if artificial intelligence and machine learning can help, that would be great. If not, that’s a decision you have to make. 

I have two examples I use in our cyber mission to illustrate the extremes here. One is for incident triage. If a cyber incident is detected, we have a triage process to make sure that it’s real. That presents information to an analyst. If that’s done correctly, it has a high benefit because it can take a lot of work off our analysts. It has low–to–medium regret if it’s done incorrectly, because the decision is to present information to an analyst who can then provide that additional filter. So that’s a high benefit, low regret. That’s a no-brainer for automating as much as possible. 

On the other side of the spectrum is protecting next generation 911 call centers from a potential telephony denial of service attack. One of the potential automated responses could be to cut off the incoming traffic to the 911 call center to stunt the attack. Benefit: you may have prevented the attack. Regret: potentially you’re cutting off legitimate traffic to a 911 call center, and that has life and safety implications. And that is unacceptable. That’s an area where automation is probably not the right approach. Those are two extreme examples, which are easy for people to understand, and it helps illustrate how the benefit regret matrix can work. How you make decisions is really the key to understanding whether to implement AI and machine learning to help automate those decisions using the full breadth of data.  

Learn more about the Cybersecurity & Infrastructure Security Agency.  

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2020 presidential debate memes: Who muted the mute button? - CNET

President Donald Trump and Sen. Joe Biden face off for the final time, but the much-promised mute button seemed to have gone missing.

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Expensify's CEO emailed all users to encourage them to vote for Biden; says "anything less than a vote for Biden is a vote against democracy" (Biz Carson/Protocol)

Biz Carson / Protocol:
Expensify's CEO emailed all users to encourage them to vote for Biden; says “anything less than a vote for Biden is a vote against democracy”  —  Some recipients were already showing anger at the email.  —  Expensify CEO David Barrett blasted all of his customers with a message to vote for Biden to “protect democracy.”

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Making Use Of AI Ethics Tuning Knobs In AI Autonomous Cars

By Lance Eliot, the AI Trends Insider  

There is increasing awareness about the importance of AI Ethics, consisting of being mindful of the ethical ramifications of AI systems.   

AI developers are being asked to carefully design and build their AI mechanizations by ensuring that ethical considerations are at the forefront of the AI systems development process. When fielding AI, those responsible for the operational use of the AI also need to be considering crucial ethical facets of the in-production AI systems. Meanwhile, the public and those using or reliant upon AI systems are starting to clamor for heightened attention to the ethical and unethical practices and capacities of AI.   

Consider a simple example. Suppose an AI application is developed to assess car loan applicants. Using Machine Learning (ML) and Deep Learning (DL), the AI system is trained on a trove of data and arrives at some means of choosing among those that it deems are loan worthy and those that are not. 

The underlying Artificial Neural Network (ANN) is so computationally complex that there are no apparent means to interpret how it arrives at the decisions being rendered. Also, there is no built-in explainability capability and thus the AI is unable to articulate why it is making the choices that it is undertaking (note: there is a movement toward including XAI, explainable AI components to try and overcome this inscrutability hurdle).   

Upon the AI-based loan assessment application being fielded, soon thereafter protests arose by some that assert they were turned down for their car loan due to an improper inclusion of race or gender as a key factor in rendering the negative decision.   

At first, the maker of the AI application insists that they did not utilize such factors and professes complete innocence in the matter. Turns out though that a third-party audit of the AI application reveals that the ML/DL is indeed using race and gender as core characteristics in the car loan assessment process. Deep within the mathematically arcane elements of the neural network, data related to race and gender were intricately woven into the calculations, having been dug out of the initial training dataset provided when the ANN was crafted. 

That is an example of how biases can be hidden within an AI system. And it also showcases that such biases can go otherwise undetected, including that the developers of the AI did not realize that the biases existed and were seemingly confident that they had not done anything to warrant such biases being included. 

People affected by the AI application might not realize they are being subjected to such biases. In this example, those being adversely impacted perchance noticed and voiced their concerns, but we are apt to witness a lot of AI that no one will realize they are being subjugated to biases and therefore not able to ring the bell of dismay.   

Various AI Ethics principles are being proffered by a wide range of groups and associations, hoping that those crafting AI will take seriously the need to consider embracing AI ethical considerations throughout the life cycle of designing, building, testing, and fielding AI.   

AI Ethics typically consists of these key principles: 

1)      Inclusive growth, sustainable development, and well-being 

2)      Human-centered values and fairness 

3)      Transparency and explainability 

4)      Robustness, security, and safety 

5)      Accountability   

We certainly expect humans to exhibit ethical behavior, and thus it seems fitting that we would expect ethical behavior from AI too.   

Since the aspirational goal of AI is to provide machines that are the equivalent of human intelligence, being able to presumably embody the same range of cognitive capabilities that humans do, this perhaps suggests that we will only be able to achieve the vaunted goal of AI by including some form of ethics-related component or capacity. 

What this means is that if humans encapsulate ethics, which they seem to do, and if AI is trying to achieve what humans are and do, the AI ought to have an infused ethics capability else it would be something less than the desired goal of achieving human intelligence.   

You could claim that anyone crafting AI that does not include an ethics facility is undercutting what should be a crucial and integral aspect of any AI system worth its salt. 

Of course, trying to achieve the goals of AI is one matter, meanwhile, since we are going to be mired in a world with AI, for our safety and well-being as humans we would rightfully be arguing that AI had better darned abide by ethical behavior, however that might be so achieved.   

Now that we’ve covered that aspect, let’s take a moment to ponder the nature of ethics and ethical behavior.  

Considering Whether Humans Always Behave Ethically   

Do humans always behave ethically? I think we can all readily agree that humans do not necessarily always behave in a strictly ethical manner.   

Is ethical behavior by humans able to be characterized solely by whether someone is in an ethically binary state of being, namely either purely ethical versus being wholly unethical? I would dare say that we cannot always pin down human behavior into two binary-based and mutually exclusive buckets of being ethical or being unethical. The real-world is often much grayer than that, and we at times are more likely to assess that someone is doing something ethically questionable, but it is not purely unethical, nor fully ethical. 

In a sense, you could assert that human behavior ranges on a spectrum of ethics, at times being fully ethical and ranging toward the bottom of the scale as being wholly and inarguably unethical. In-between there is a lot of room for how someone ethically behaves. 

If you agree that the world is not a binary ethical choice of behaviors that fit only into truly ethical versus solely unethical, you would therefore also presumably be amenable to the notion that there is a potential scale upon which we might be able to rate ethical behavior. 

This scale might be from the scores of 1 to 10, or maybe 1 to 100, or whatever numbering we might wish to try and assign, maybe even including negative numbers too. 

Let’s assume for the moment that we will use the positive numbers of a 1 to 10 scale for increasingly being ethical (the topmost is 10), and the scores of -1 to -10 for being unethical (the -10 is the least ethical or in other words most unethical potential rating), and zero will be the midpoint of the scale. 

Please do not get hung up on the scale numbering, which can be anything else that you might like. We could even use letters of the alphabet or any kind of sliding scale. The point being made is that there is a scale, and we could devise some means to establish a suitable scale for use in these matters.   

The twist is about to come, so hold onto your hat.   

We could observe a human and rate their ethical behavior on particular aspects of what they do. Maybe at work, a person gets an 8 for being ethically observant, while perhaps at home they are a more devious person, and they get a -5 score. 

Okay, so we can rate human behavior. Could we drive or guide human behavior by the use of the scale? 

Suppose we tell someone that at work they are being observed and their target goal is to hit an ethics score of 9 for their first year with the company. Presumably, they will undertake their work activities in such a way that it helps them to achieve that score.   

In that sense, yes, we can potentially guide or prod human behavior by providing targets related to ethical expectations. I told you a twist was going to arise, and now here it is. For AI, we could use an ethical rating or score to try and assess how ethically proficient the AI is.   

In that manner, we might be more comfortable using that particular AI if we knew that it had a reputable ethical score. And we could also presumably seek to guide or drive the AI toward an ethical score too, similar to how this can be done with humans, and perhaps indicate that the AI should be striving towards some upper bound on the ethics scale. 

Some pundits immediately recoil at this notion. They argue that AI should always be a +10 (using the scale that I’ve laid out herein). Anything less than a top ten is an abomination and the AI ought to not exist. Well, this takes us back into the earlier discussion about whether ethical behavior is in a binary state.   

Are we going to hold AI to a “higher bar” than humans by insisting that AI always be “perfectly” ethical and nothing less so?   

This is somewhat of a quandary due to the point that AI overall is presumably aiming to be the equivalent of human intelligence, and yet we do not hold humans to that same standard. 

For some, they fervently believe that AI must be held to a higher standard than humans. We must not accept or allow any AI that cannot do so. 

Others indicate that this seems to fly in the face of what is known about human behavior and begs the question of whether AI can be attained if it must do something that humans cannot attain.   

Furthermore, they might argue that forcing AI to do something that humans do not undertake is now veering away from the assumed goal of arriving at the equivalent of human intelligence, which might bump us away from being able to do so as a result of this insistence about ethics.   

Round and round these debates continue to go. 

Those on the must-be topnotch ethical AI are often quick to point out that by allowing AI to be anything less than a top ten, you are opening Pandora’s box. For example, it could be that AI dips down into the negative numbers and sits at a -4, or worse too it digresses to become miserably and fully unethical at a dismal -10. 

Anyway, this is a debate that is going to continue and not be readily resolved, so let’s move on. 

If you are still of the notion that ethics exists on a scale and that AI might also be measured by such a scale, and if you also are willing to accept that behavior can be driven or guided by offering where to reside on the scale, the time is ripe to bring up tuning knobs. Ethics tuning knobs. 

Here’s how that works. You come in contact with an AI system and are interacting with it. The AI presents you with an ethics tuning knob, showcasing a scale akin to our ethics scale earlier proposed. Suppose the knob is currently at a 6, but you want the AI to be acting more aligned with an 8, so you turn the knob upward to the 8. At that juncture, the AI adjusts its behavior so that ethically it is exhibiting an 8-score level of ethical compliance rather than the earlier setting of a 6. 

What do you think of that? 

Some would bellow out balderdash, hogwash, and just unadulterated nonsense. A preposterous idea or is it genius? You’ll find that there are experts on both sides of that coin. Perhaps it might be helpful to provide the ethics tuning knob within a contextual exemplar to highlight how it might come to play. 

Here’s a handy contextual indication for you: Will AI-based true self-driving cars potentially contain an ethics tuning knob for use by riders or passengers that use self-driving vehicles?   

Let’s unpack the matter and see.   

For my framework about AI autonomous cars, see the link here: https://aitrends.com/ai-insider/framework-ai-self-driving-driverless-cars-big-picture/ 

Why this is a moonshot effort, see my explanation here: https://aitrends.com/ai-insider/self-driving-car-mother-ai-projects-moonshot/ 

For more about the levels as a type of Richter scale, see my discussion here: https://aitrends.com/ai-insider/richter-scale-levels-self-driving-cars/ 

For the argument about bifurcating the levels, see my explanation here: https://aitrends.com/ai-insider/reframing-ai-levels-for-self-driving-cars-bifurcation-of-autonomy/   

Understanding The Levels Of Self-Driving Cars   

As a clarification, true self-driving cars are ones that the AI drives the car entirely on its own and there isn’t any human assistance during the driving task.   

These driverless vehicles are considered a Level 4 and Level 5, while a car that requires a human driver to co-share the driving effort is usually considered at a Level 2 or Level 3. The cars that co-share the driving task are described as being semi-autonomous, and typically contain a variety of automated add-on’s that are referred to as ADAS (Advanced Driver-Assistance Systems).   

There is not yet a true self-driving car at Level 5, which we don’t yet even know if this will be possible to achieve, and nor how long it will take to get there. 

Meanwhile, the Level 4 efforts are gradually trying to get some traction by undergoing very narrow and selective public roadway trials, though there is controversy over whether this testing should be allowed per se (we are all life-or-death guinea pigs in an experiment taking place on our highways and byways, some contend). 

Since semi-autonomous cars require a human driver, the adoption of those types of cars won’t be markedly different than driving conventional vehicles, so there’s not much new per se to cover about them on this topic (though, as you’ll see in a moment, the points next made are generally applicable).   

For semi-autonomous cars, it is important that the public needs to be forewarned about a disturbing aspect that’s been arising lately, namely that despite those human drivers that keep posting videos of themselves falling asleep at the wheel of a Level 2 or Level 3 car, we all need to avoid being misled into believing that the driver can take away their attention from the driving task while driving a semi-autonomous car.   

You are the responsible party for the driving actions of the vehicle, regardless of how much automation might be tossed into a Level 2 or Level 3. 

For why remote piloting or operating of self-driving cars is generally eschewed, see my explanation here: https://aitrends.com/ai-insider/remote-piloting-is-a-self-driving-car-crutch/ 

To be wary of fake news about self-driving cars, see my tips here: https://aitrends.com/ai-insider/ai-fake-news-about-self-driving-cars/ 

The ethical implications of AI driving systems are significant, see my indication here: http://aitrends.com/selfdrivingcars/ethically-ambiguous-self-driving-cars/   

Be aware of the pitfalls of normalization of deviance when it comes to self-driving cars, here’s my call to arms: https://aitrends.com/ai-insider/normalization-of-deviance-endangers-ai-self-driving-cars/   

Self-Driving Cars And Ethics Tuning Knobs 

For Level 4 and Level 5 true self-driving vehicles, there won’t be a human driver involved in the driving task. All occupants will be passengers. The AI is doing the driving.   

This seems rather straightforward. You might be wondering where any semblance of ethics behavior enters the picture. Here’s how. Some believe that a self-driving car should always strictly obey the speed limit. 

Imagine that you have just gotten into a self-driving car in the morning and it turns out that you are possibly going to be late getting to work. Your boss is a stickler and has told you that coming in late is a surefire way to get fired.   

You tell the AI via its Natural Language Processing (NLP) that the destination is your work address. 

And, you ask the AI to hit the gas, push the pedal to the metal, screech those tires, and get you to work on-time.

But it is clear cut that if the AI obeys the speed limit, there is absolutely no chance of arriving at work on-time, and since the AI is only and always going to go at or less than the speed limit, your goose is fried.   

Better luck at your next job.   

Whoa, suppose the AI driving system had an ethics tuning knob. 

Abiding strictly by the speed limit occurs when the knob is cranked up to the top numbers like say 9 and 10. 

You turn the knob down to a 5 and tell the AI that you need to rush to work, even if it means going over the speed limit, which at a score of 5 it means that the AI driving system will mildly exceed the speed limit, though not in places like school zones, and only when the traffic situation seems to allow for safely going faster than the speed limit by a smidgen.   

The AI self-driving car gets you to work on-time!   

Later that night, when heading home, you are not in as much of a rush, so you put the knob back to the 9 or 10 that it earlier was set at. 

Also, you have a child-lock on the knob, such that when your kids use the self-driving car, which they can do on their own since there isn’t a human driver needed, the knob is always set at the topmost of the scale and the children cannot alter it.   

How does that seem to you? 

Some self-driving car pundits find the concept of such a tuning knob to be repugnant. 

They point out that everyone will “cheat” and put the knob on the lower scores that will allow the AI to do the same kind of shoddy and dangerous driving that humans do today. Whatever we might have otherwise gained by having self-driving cars, such as the hoped-for reduction in car crashes, along with the reduction in associated injuries and fatalities, will be lost due to the tuning knob capability.   

Others though point out that it is ridiculous to think that people will put up with self-driving cars that are restricted drivers that never bend or break the law. 

You’ll end-up with people opting to rarely use self-driving cars and will instead drive their human-driven cars. This is because they know that they can drive more fluidly and won’t be stuck inside a self-driving car that drives like some scaredy-cat. 

As you might imagine, the ethical ramifications of an ethics tuning knob are immense. 

In this use case, there is a kind of obviousness about the impacts of what an ethics tuning knob foretells.   

Other kinds of AI systems will have their semblance of what an ethics tuning knob might portend, and though it might not be as readily apparent as the case of self-driving cars, there is potentially as much at stake in some of those other AI systems too (which, like a self-driving car, might entail life-or-death repercussions).   

For why remote piloting or operating of self-driving cars is generally eschewed, see my explanation here: https://aitrends.com/ai-insider/remote-piloting-is-a-self-driving-car-crutch/   

To be wary of fake news about self-driving cars, see my tips here: https://aitrends.com/ai-insider/ai-fake-news-about-self-driving-cars/ 

The ethical implications of AI driving systems are significant, see my indication here: http://aitrends.com/selfdrivingcars/ethically-ambiguous-self-driving-cars/   

Be aware of the pitfalls of normalization of deviance when it comes to self-driving cars, here’s my call to arms: https://aitrends.com/ai-insider/normalization-of-deviance-endangers-ai-self-driving-cars/   

Conclusion   

If you really want to get someone going about the ethics tuning knob topic, bring up the allied matter of the Trolley Problem.   

The Trolley Problem is a famous thought experiment involving having to make choices about saving lives and which path you might choose. This has been repeatedly brought up in the context of self-driving cars and garnered acrimonious attention along with rather diametrically opposing views on whether it is relevant or not. 

In any case, the big overarching questions are will we expect AI to have an ethics tuning knob, and if so, what will it do and how will it be used. 

Those that insist there is no cause to have any such device are apt to equally insist that we must have AI that is only and always practicing the utmost of ethical behavior. 

Is that a Utopian perspective or can it be achieved in the real world as we know it?   

Only my crystal ball can say for sure.  

Copyright 2020 Dr. Lance Eliot  

This content is originally posted on AI Trends.  

[Ed. Note: For reader’s interested in Dr. Eliot’s ongoing business analyses about the advent of self-driving cars, see his online Forbes column: https://forbes.com/sites/lanceeliot/] 

http://ai-selfdriving-cars.libsyn.com/website 

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