Oversight of AI is the board's job, regardless of the subject matter complexity. One of the most consequential challenges confronting corporate governance in the near term will be its ability to exercise informed oversight over the application of artificial intelligence (AI) within its organization. It will be a challenge that will arise regardless of the industry sector in which the company operates, and regardless of how it applies AI in that operation. The essence of the challenge is the rapidly emerging conflict between the perceived societal and commercial benefits arising from AI implementation, and the perceived societal and institutional risks arising from its use. The need to address the challenge is urgent; the competing interests of benefit and risk are hurtling at each other at hypersonic speed.

Comment Future AI could be a challenge for US Patent and Trademark Office (USPTO) officials, who need to wrap their heads around complex technology that's perhaps not quite compatible with today's laws. Under the Department of Commerce, the USPTO's core mission is to protect intellectual property, or IP. Creators file patent applications in hope of keeping competitors from copying their inventions without permission, and patents are supposed to allow businesses to thrive with their own novel designs while not stifling wider innovation. Fast evolving technologies, such as deep learning, are pushing the limits of today's IP policies and rules. Clerks are trying to apply traditional patent approval rules to non-trivial machine-learning inventions, and bad decisions could result in a stranglehold on competition among public and private AI creators.

Online dating company Match Group wants the court proceedings in an investigation being carried out by the U.S. Federal Trade Commission (FTC) for allegedly sharing users' photos with a facial recognition company to proceed in secret. The news comes from a Reuters investigation, after it spotted an FTC petition filed on 26 May forcing Match to provide documents related to an alleged 2014 data-sharing deal between Match subsidiary OkCupid and biometric solutions provider Clarifai. The background is that in 2019 a New York Times article claimed that Clarifai built its database of faces for biometric algorithm training using OkCupid user photos provided by an OkCupid founder and Clarifai investor. At the time, both OkCupid and Match denied any commercial agreement with Clarifai, so in 2020 the FTC followed up by demanding documentation around that alleged deal. A lawsuit filed under Illinois' BIPA was dismissed for lack of jurisdiction, and the companies hid behind attorney-client and work-product privilege to avoid providing the requested 136 documents, which in turn led to May's petition by the FTC.

Modern supply chains have reached an unprecedented and rather extraordinary level of complexity. The increasing digitization of the physical world, in conjunction with the innovation of IoT technology and sophisticated personality-based targeted marketing techniques, has created an ecosystem where consumer markets are instantaneously accessible, adaptable, and trackable. While there is an enormous potential for profit on the side of businesses, increases in demand have made the maintenance of a linearly structured supply chain unfeasible, especially when it is no longer strictly defined by its transition from raw material to final product. Moreover, consumers, businesses and suppliers now form a multi-dimensional conglomerate that ranges beyond specific industry-domains, population demographics, and national borders. These dynamics have outlined the vulnerability of our existing supply chains.

India's Central Information Commission (CIC) has criticized the Delhi Police for giving "patently incorrect" replies to Right to Information (RTI) requests on its use of face detection technology during riots in northeast Delhi in 2020, reports Mint. The CIC, a body set up to help individuals acquire information when other routes fail, has insisted that the Delhi Police provide revised responses to information requests. According to the report, the CIC said the Delhi Police responses suffer from "legal infirmities" and demonstrate "no application of mind." There appear to be eight or nine findings against Delhi Police on the Decisions section of the CIC website concerning the issues of the riots and other cases, outlining the back and forth between appellant and the respondent, including scans of the handwritten correspondence. The CIC has asked for clarity on the use of its face detection technology, its accuracy, what database was used for comparison and whether biometrics were used in the North East Delhi Riots, where Hindu mobs attacked Muslims and their properties, leaving more than 50 dead, the majority Muslims, reported the Guardian.

Artificial intelligence has been making major headway in many areas of the workforce, bringing considerable changes to fields such as architecture, agriculture, sports analytics, etc., and even the law industry. Improving so many fields of work, how has artificial intelligence changed the law industry for the better? Artificial intelligence has improved the law industry in several ways, such as data processing and legal research, generating the content, and decreasing overall stress. In addition, artificial intelligence has made the law industry more productive, giving lawyers more time to focus on their clients and cases rather than tedious paperwork and information. The rest of this article will describe how artificial intelligence has improved the law industry. Artificial intelligence (AI) has improved data processing and research in law practices.

Synthetic biology is an emerging field that involves the engineering and re-design of organisms for purposes such as food security, health, and environmental protection. As such, it poses numerous ethical, legal, and social implications (ELSI) for researchers and policy makers. Various efforts to ensure socially responsible synthetic biology are underway. Policy making is one regulatory avenue, and other initiatives have sought to embed social scientists and ethicists on synthetic biology projects. However, given the nascency of synthetic biology, the number of heterogeneous domains it spans, and the open nature of many ethical questions, it has proven challenging to establish widespread concrete policies, and including social scientists and ethicists on synthetic biology teams has met with mixed success. This text proposes a different approach, asking instead is it possible to develop a well-performing recommender model based upon natural language processing (NLP) to connect synthetic biologists with information on the ELSI of their specific research? This recommender was developed as part of a larger project building a Synthetic Biology Knowledge System (SBKS) to accelerate discovery and exploration of the synthetic biology design space. Our approach aims to distill for synthetic biologists relevant ethical and social scientific information and embed it into synthetic biology research workflows.

In this work we use Equal Oppportunity (EO) doctrines from political philosophy to make explicit the normative judgements embedded in different conceptions of algorithmic fairness. We contrast formal EO approaches that narrowly focus on fair contests at discrete decision points, with substantive EO doctrines that look at people's fair life chances more holistically over the course of a lifetime. We use this taxonomy to provide a moral interpretation of the impossibility results as the incompatibility between different conceptions of a fair contest -- foward-facing versus backward-facing -- when people do not have fair life chances. We use this result to motivate substantive conceptions of algorithmic fairness and outline two plausible fair decision procedures based on the luck-egalitarian doctrine of EO, and Rawls's principle of fair equality of opportunity. Equality of Opportunity (EO) is a philosophical doctrine that objects to morally arbitrary and irrelevant factors affecting people's access to desirable positions, and the social goods attached to them (such as opportunity and wealth). In an EO-respecting society, all people, irrespective of their morally arbitrary characteristics, such as socio-economic background, gender, race, or disability status, have comparable access to the opportunities that they desire. Similarly, in fair machine learning (fair-ML), we are usually interested in ensuring that the outputs of algorithmic systems, specially those used in critical social contexts, do not systematically skew along the lines of membership in protected groups based on gender, race, or disability. In so far as protected groups are constructed on the basis of morally arbitrary factors, the moral desiderata of EO doctrines from political philosophy align exactly with the fairness-related concerns in machine learning. In this work, we employ ideas from the rich literature on Equality of Opportunity from political philosophy [1-11] to clarify the normative foundations of fairness and justice-related interventions, and gauge the efficacy of current algorithmic approaches that attempt to codify these criteria. There are two broad principles of EO, namely, the principle of fair contests and the principle of fair life chances. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. The principle of fair contests, commonly understood as the nondiscrimination principle, says that competitions for desirable positions should be open to all and should be adjudicated based on competitors' relevant merits, or qualifications.

Artificial Intelligence ("AI") is often seen as an imminent threat to employment opportunities. According to the OECD, however, AI is more likely to become integrated with certain tasks within a role than result in net job losses. On 23 June 2022, the Expert Group on Future Skills Needs (Irish government advisory group) published a report considering the skills needed in the Irish workforce to benefit from the opportunities presented by AI (the Report). While acknowledging that AI will have an impact on almost every sector of the economy and society, the Report highlighted the current skills gap as the most significant hurdle to AI development in Ireland. The Report finds that due to AI being a "general-purpose technology", it will have a broad impact on the labour market by improving efficiency, reducing costs, improving service offerings, and supporting decision making for firms.

The Federal Trade Commission delivered a report to Congress warning about the use of artificial intelligence to combat online harms. The June 16 report lays out the FTC's latest thinking on AI, and any organization that uses algorithmic decision-making in a way that impacts consumers should take heed. Key takeaways include: The importance (and limitations) of having a human in the loop. The need for AI to be "meaningfully transparent, which includes the need for it to be explainable and contestable, especially when people's rights are involved or when personal data is being collected or used." Companies that use AI "must be accountable both for their data practices and for their results" and should consider independent audits and algorithmic impact assessments.