The UK government has announced an initial impact review in response to the continued growth and concerns around generative AI and learning language models. The investigation will reportedly look at how the creation and distribution of AI technology impact five wide-reaching areas: appropriate transparency and explainability; accountability and governance; safety, security and robustness; fairness; and contestability and redress. Overall, the review aims to learn how AI foundation models can, and likely will, impact both competition and consumer protections. Regulating bodies tasked with finding the answers include the Competition and Markets Authority (CMA), which helps people and businesses in competitive markets while working against unethical practices. "It's crucial that the potential benefits of this transformative technology are readily accessible to UK businesses and consumers while people remain protected from issues like false or misleading information," Sarah Cardell, CMA's chief executive, said in a statement.

The rapid advancement of Artificial Intelligence (AI) technologies has already transformed business operations across the globe. From customer service chat-bots to adaptive cybersecurity, the applications of AI are nearly limitless. When properly designed, AI can help minimize paperwork, reduce costs, and drive better business decisions by increasing the predictive accuracy of future outcomes and mitigating the cognitive biases inherent in human decision making. In the financial services industry, AI has the potential to expand access to affordable credit for consumers and small businesses and combat fraud and financial crimes, but many financial institutions remain reluctant to deploy AI to its maximum potential without clear guidance from US regulatory agencies. Like many new technologies, the current AI landscape lacks a depth of established legal and regulatory precedent to rely on.

Among the recommendations are the introduction of suitability requirements for AI-driven financial services, and add-on capital buffers based on AI algorithms. The OECD has published a new report offering policy recommendations to ensure the use of artificial intelligence (AI), machine learning (ML) and big data in finance is consistent with financial stability, consumer protection, and market integrity and competition objectives. While noting that AI can drive competitive advantages for financial firms, improve their efficiency, and enhance services for consumers, the report says AI applications in finance may create or intensify financial and non-financial risks, and give rise to potential financial consumer and investor protection concerns around the fairness of consumer results, data management and data usage. The report says emerging risks from the deployment of AI techniques need to be identified and mitigated to support and promote the use of responsible AI, and existing regulatory and supervisory requirements may need to be clarified and adjusted to address incompatibilities of existing arrangements with AI applications. In particular, policymakers should consider sharpening their focus on better data governance by financial sector firms to reinforce consumer protection across AI applications in finance, and address risks related to data privacy, confidentiality, concentration of data, and unintended bias and discrimination.

In the June issue of Communications, Editor-in-Chief Andrew A. Chien suggested in his Editor's Letter (p. 5) that ACM consider bestowing two A.M. Turing Awards per year. Immediately upon reading your June Editor's letter, my reaction was "No!" because I thought two annual awards would reduce the stature of each and minimize the honor to recipients and even to Alan Turing. But I was hasty in forming my opinion. I reread your argument and changed my opinion--I now believe we need to think even bigger. The number "two" suggests a division between hardware and software.

American households are increasingly connected internally through the use of artificially intelligent appliances.1 But who regulates the safety of those dishwashers, microwaves, refrigerators, and vacuums powered by artificial intelligence (AI)? On March 2, 2021, at a virtual forum attended by stakeholders across the entire industry, the Consumer Product Safety Commission (CPSC) reminded us all that it has the last say on regulating AI and machine learning consumer product safety. The CPSC is an independent agency comprised of five commissioners who are nominated by the president and confirmed by the Senate to serve staggered seven-year terms. With the Biden administration's shift away from the deregulation agenda of the prior administration and three potential opportunities to staff the commission, consumer product manufacturers, distributors, and retailers should expect increased scrutiny and enforcement.2

Artificial intelligence requires carefully considered regulation to ensure technologies balance cooperation and competition for the greater good, according to expert speakers at the AI Summit 2020. As a general purpose technology, artificial intelligence (AI) can be used in a staggering array of contexts, with many advocates framing its rapid development as a cooperative endeavour for the benefit of all humanity. The United Nations, for example, launched it's AI for Good initiative in 2017, while the French and Chinese governments talk of "AI for Humanity" and "AI for the benefit of mankind" respectively – rhetoric echoed by many other governments and supra-national bodies across the world. On the other hand, these same advocates also use language and rhetoric that emphasises the competitive advantages AI could bring in the more narrow pursuit of national interest. "Just as in international politics, there's a tension between an agreed aspiration to build AI for humanity, and for the common good, and the more selfish and narrow drive to compete to have advantage," said Allan Dafoe, director of the Centre for the Governance of AI at Oxford University, speaking at the AI Summit, which took place online this week.

At the beginning of 2021, the European Commission will propose legislation on AI that will be, at first instance, horizontal (as opposed to sectoral) and risk-based, with mandatory requirements for high-risk AI applications. The new rules will aim at ensuring transparency, accountability and consumer protection, including safety, through robust AI governance and data quality requirements. Europe's approach to regulating technology is based on the precautionary principle, which enables rapid regulatory intervention in the face of possible danger to human, animal or plant health, or to protect the environment. This perspective has helped Europe to become a global leader in the shaping of the digital technology market. Particularly, with the introduction of the General Data Protection Regulation (GDPR) in 2018, Europe considers it has gained a competitive advantage through the creation of a trust mark for increased privacy protection. Australia and New Zealand have a close relationship with the European Union (EU) and its member countries historically.

The Consumer Financial Protection Bureau ("CFPB") has published guidance on July 7, 2020 which highlights the potential use of Artificial Intelligence ("AI") in the delivery of financial services--particularly in credit underwriting models. In addition to providing an overview of the ways in which AI is being used by financial institutions, the publication addresses: (1) industry uncertainty about how AI fits into the existing regulatory framework, especially for credit underwriting; and (2) the tools that the CFPB has been using to promote innovation, facilitate compliance, and reduce regulatory uncertainty. As the publication notes, financial institutions are starting to deploy AI across a range of functions, including as virtual assistants that can fulfill customer requests, in models to detect fraud or other potential illegal activity, or as compliance monitoring tools. Credit underwriting is one specific area in which AI may have a profound impact. Credit underwriting models that are built upon AI have the potential to expand credit access by permitting lenders to evaluate creditworthiness of some of the millions of consumers who are "unscorable" using traditional underwriting systems.

Agriculture finance represents an important element of eradicating extreme poverty and boosting shared prosperity. According to the International Fund for Agricultural Development, smallholders manage over 80% of the world's estimated 500 million small farms and provide over 80% of the food consumed in a significant part of the developing world, making a major contribution to poverty reduction and food security. Most smallholder farms are in Asia and sub-Saharan Africa, and in both regions over 80% of farmland is managed by smallholders. Even though these farmers are generally characterized by limited resources--particularly in terms of land--and dependence on household members for farm labor, they represent a critical part of food systems in developing countries. In light of the size and importance of the smallholder farming sector, the development community has a growing focus on providing these farmers with the funding they need to thrive.

In the early 1970s, the fledgling credit card industry routinely and shortsightedly held cardholders liable for fraudulent transactions, even if their cards had been lost or stolen. In response, Congress passed the 1974 Fair Credit Billing Act to limit cardholder liability. This protection increased public trust in the new payment system and spurred growth and innovation. Because they could no longer just pass fraud losses on to cardholders, payment networks devised one of the first commercial applications of neural networks to detect out-of-pattern card usage and reduce their fraud losses. Smart regulation, like the above example, that gets out in front of emerging technology can protect consumers and drive innovation.