The first attempts to regulate artificial intelligence programs that play a hidden role in hiring, housing and medical decisions for millions of Americans are facing pressure from all sides and floundering in statehouses nationwide. Only one of seven bills aimed at preventing AI's penchant to discriminate when making consequential decisions -- including who gets hired, money for a home or medical care -- has passed. Colorado Gov. Jared Polis hesitantly signed the bill on Friday. Colorado's bill and those that faltered in Washington, Connecticut and elsewhere faced battles on many fronts, including between civil rights groups and the tech industry, and lawmakers wary of wading into a technology few yet understand and governors worried about being the odd-state-out and spooking AI startups. Polis signed Colorado's bill "with reservations," saying in an statement he was wary of regulations dousing AI innovation.

Twitter CEO Elon Musk provides insight on the consequences of developing artificial intelligence and the potential impact on elections on "Tucker Carlson Tonight." Congress is under increasing pressure from technology giants and others to find a way to regulate artificial intelligence, and a likely candidate for early action is a bill that both Republicans and Democrats supported in the last Congress under Democrat leadership. In 2022, the House Energy and Commerce Committee passed the American Data Privacy and Protection Act (ADPPA), a bill that's aimed at boosting data privacy rights but would also play a big role in regulating emerging AI systems. The ADPPA won almost unanimous support from both parties last year and continues to be supported by companies that are eager to build trust in their AI products, and they believe that a federal regulatory structure will help them get there. BSA/Software Alliance represents dozens of companies, including Microsoft, Okta, Salesforce and others, that build software and AI tools that companies use to run their businesses. BSA is working closely with the committee to get a version of that bill passed this year that it hopes can be approved in a full House vote.

It is my view that deficiencies in the current classification system undermine our national security, as well as critical democratic objectives, by impeding our ability to share information in a timely manner

When you harness the power and potential of machine learning, there are also some drastic downsides that you've got to manage. Deploying machine learning, you face the risk that it be discriminatory, biased, inequitable, exploitative, or opaque. In this article, I cover six ways that machine learning threatens social justice – linking to short videos that dive deeply into each one – and reach an incisive conclusion: The remedy is to take on machine learning standardization as a form of social activism. When you use machine learning, you aren't just optimizing models and streamlining business. In essence, the models embody policies that control access to opportunities and resources for many people.

When you harness the power and potential of machine learning, there are also some drastic downsides that you've got to manage. Deploying machine learning, you face the risk that it be discriminatory, biased, inequitable, exploitative, or opaque. In this article, I cover six ways that machine learning threatens social justice and reach an incisive conclusion: The remedy is to take on machine learning standardization as a form of social activism. When you use machine learning, you aren't just optimizing models and streamlining business. In essence, the models embody policies that control access to opportunities and resources for many people.

When organizations use algorithms to make decisions, biases built into the underlying data create not just challenges but also engender enormous risk. What should companies do to manage such risks? The way forward is to conduct artificial intelligence (AI) audits, according to this opinion piece by Kartik Hosanagar, a Wharton professor of operations, information and decisions who studies technology and the digital economy. This column is based on ideas from his book, A Human's Guide to Machine Intelligence. Much has been written about challenges associated with AI-based decisions. Some documented failures include gender and race biases in recruiting and credit approval software; chatbots that turned racist and driverless cars that fail to recognize stop signs due to adversarial attacks; inaccuracies in predictive models for public health surveillance; and diminished trust because of the difficulty we have interpreting certain machine learning models.

Intel CEO, Brian Krzanich, announced that Intel Capital will invest $250m in the next two years in the autonomous vehicle (AV) ecosystem, focused on problems in connectivity, communication, context awareness, deep learning, security and safety. When viewed in the context of the fund's short-lived intention to sell $1bn worth of portfolio holdings in March this year (it was cancelled in May), I think this shows Intel is serious on going long with AI. Indeed, the company purchased recently Nervana Systems and Movidius, which could help it's larger AV program and the race against NVIDIA. Nauto, the startup offering a direct to consumer network of cloud-connected dashboard cameras applied to car insurance, inked a data sharing agreement and investment from Toyota Research Institute, BMW iVentures and Allianz Ventures (thanks Moritz for sharing!). One of the reasons for the immense progress in AI is data crowdsourcing.