On 21 April 2021, the European Commission published draft regulations ("AI Regulations") governing the use of artificial intelligence (AI). The European Parliament and the member states have not yet adopted these proposed AI Regulations. The European Commission's proposed AI Regulations are the first attempt the world has seen at creating a uniform legal framework governing the use, development and marketing of AI. They will likely have a resounding impact on all businesses that use AI for years to come. The AI Regulations will become effective 20 days after publication in the Official Journal.

The 3rd chapter of the textbook provides an overview of ethical issues that exist in the field of artificial intelligence. It provides cautionary tales, unintended consequences, and ethical considerations. It also covers biases that cause ethical issues and some tools that can help address them. We've spent many weeks writing the questionnaires. And the reason for that, is because we tried to think about what we wanted you to take away from each chapter.

Shortly after Microsoft announced it was laying off scores of journalists across its news divisions and replacing them with news-skimming artificial intelligence, it's already in hot water after a clear example of racial algorithmic bias. The algorithm doesn't do any original reporting. Instead, it finds articles on the internet and populates MSN with them. Recently, it confused two women of color from the band Little Mix with each other, The Guardian reports, attaching an image of Leigh-Anne Pinnock to an article about singer Jade Thirlwall. It's an unfortunate illustration of how racism continues to persist within AI algorithms, which are notoriously bad at recognizing people of color.

LENs is a Python module integrating a variety of state-of-the-art approaches to provide logic explanations from neural networks. This package focuses on bringing these methods to non-specialists. It has minimal dependencies and it is distributed under the Apache 2.0 licence allowing both academic and commercial use. Source code and documentation can be downloaded from the github repository: https://github.com/pietrobarbiero/logic_explainer_networks.

Our last AI post on this blog, the New (if Decidedly Not'Final') Frontier of Artificial Intelligence Regulation, touched on both the Federal Trade Commission's (FTC) April 19, 2021, AI guidance and the European Commission's proposed AI Regulation. The recent FTC guidance also relied on older FTC work on AI, including a January 2016 report, "Big Data: A Tool for Inclusion or Exclusion?," The Big Data workshop addressed data modeling, data mining and analytics, and gave us a prospective look at what would become an FTC strategy on AI. The FTC's guidance begins with the data, and the 2016 guidance on big data and subsequent AI development addresses this most directly. The 2020 guidance then highlights important principles such as transparency, explain-ability, fairness, accuracy and accountability for organizations to consider.

Amazon will extend a ban it enacted last year on the use of its facial recognition for law enforcement purposes. The web giant's Rekognition service is one of the most powerful facial recognition tools available. Last year, Amazon signed a one-year moratorium that banned its use by police departments following a string of cases where facial recognition services – from various providers – were found to be inaccurate and/or misused by law enforcement. Amazon has now extended its ban indefinitely. Facial recognition services have already led to wrongful arrests that disproportionally impacted marginalised communities.

Today, publicity around highly touted but underperforming AI solutions has placed the health sector at risk for another AI winter. To respond to this challenge, we propose that industry organizations consider implementing self-governance standards to better mitigate risks and encourage greater trust in AI capabilities. Building on the National Academy of Medicine's AI implementation lifecycle, we created a detailed organizational framework that identifies 10 groups of AI risks and 14 groups of mitigation practices across the four lifecycle phases. AI developers, implementers, and other stakeholders can use this analysis to guide collective, voluntary actions to select, establish, and track adherence to trust-enhancing AI standards. Without industry self-governance, government agencies may act to institute their own compliance requirements. However, industries that have proactively defined, adopted, and implemented standards complementary to government regulation have reduced the urgency of public-sector action while allowing for the appropriate use of available resources.

Alongside such attacks, we've seen new impetus behind the regulation of artificial intelligence (AI), with the world's first regulatory framework for the …

Building on a pilot partnership with an artificial intelligence company it launched last year, Baker McKenzie is upping its bet that machine learning and data-driven analytics will benefit the firm and its clients. Baker McKenzie, which first teamed up with AI-powered platform SparkBeyond in October, is now entering into a three-year exclusive contract with the company and building a new 11-person team within the firm to leverage the technology for internal and client-facing projects, the global firm said Monday. The firm plans to hire two co-founders to build out and lead the new team alongside London-based partner Ben Allgrove, who is Baker McKenzie's global head of research and development. The firm said candidates for the roles - which are now open for applications - should be "steeped in legal innovation." "Five years ago our industry was flooded with hype about AI disruption," Allgrove said in a statement.

Over the last few weeks, regulators and lawmakers around the world have made one thing clear: New laws will soon shape how companies use artificial intelligence (AI). In late March, the five largest federal financial regulators in the United States released a request for information on how banks use AI, signaling that new guidance is coming for the finance sector. Just a few weeks after that, the U.S. Federal Trade Commission (FTC) released an uncharacteristically bold set of guidelines on "truth, fairness, and equity" in AI -- defining unfairness, and therefore the illegal use of AI, broadly as any act that "causes more harm than good." The European Commission followed suit on April 21 released its own proposal for the regulation of AI, which includes fines of up to 6% of a company's annual revenues for noncompliance -- fines that are higher than the historic penalties of up to 4% of global turnover that can be levied under the General Data Protection Regulation (GDPR). For companies adopting AI, the dilemma is clear: On the one hand, evolving regulatory frameworks on AI will significantly impact their ability to use the technology; on the other, with new laws and proposals still evolving, it can seem like it's not yet clear what companies can and should do.