Clearview has built a product no other company has been willing to build, because of its dangerousness, and this settlement vindicates the decision

Notorious facial recognition company Clearview AI has agreed to permanently halt sales of its massive biometric database to all private companies and individuals in the United States as part of a legal settlement with the American Civil Liberties Union, per court records. Monday's announcement marks the close of a two-year legal dispute brought by the ACLU and privacy advocate groups in May of 2020 against the company over allegations that it had violated BIPA, the 2008 Illinois Biometric Information Privacy Act. This act requires companies to obtain permission before harvesting a person's biometric information -- fingerprints, gait metrics, iris and face scans for example -- and empowers users to sue the companies who do not. In addition to the nationwide private party sales ban, Clearview will not offer any of its services to Illinois local and state law enforcement agencies (as well as all private parties) for the next five years. "This means that within Illinois, Clearview cannot take advantage of BIPA's exception for government contractors during that time," the ACLU points out, though Federal agencies, state and local law enforcement departments outside of Illinois will be unaffected.

Tesla has sued a former employee who it is accusing of stealing trade secrets related to its supercomputer project, Bloomberg reported on Friday. According to a filing in the U.S. District Court in San Jose, thermal engineer Alexander Yatskov quit on May 2 after having joined the company only a few months earlier, in January. According to Tesla, Yatskov admitted to transferring confidential information to his personal devices and later handing over a "dummy" laptop after company officials confronted him on suspicion of theft. In addition to breaching a non-disclosure agreement intended to protect trade secrets, Bloomberg reports that Tesla is also accusing Yatskov of misrepresenting his experience and skills on his resume. Bloomberg also says that Yatskov declined to comment.

Secretary shall support a program of fundamental research, development, and demonstration of energy efficient computing and data center technologies relevant to advanced computing applications, including high performance computing, artificial intelligence, and scientific machine learning.").

To foster usefulness and accountability of machine learning (ML), it is essential to explain a model's decisions in addition to evaluating its performance. Accordingly, the field of explainable artificial intelligence (XAI) has resurfaced as a topic of active research, offering approaches to address the "how" and "why" of automated decision-making. Within this domain, counterfactual explanations (CFEs) have gained considerable traction as a psychologically grounded approach to generate post-hoc explanations. To do so, CFEs highlight what changes to a model's input would have changed its prediction in a particular way. However, despite the introduction of numerous CFE approaches, their usability has yet to be thoroughly validated at the human level. Thus, to advance the field of XAI, we introduce the Alien Zoo, an engaging, web-based and game-inspired experimental framework. The Alien Zoo provides the means to evaluate usability of CFEs for gaining new knowledge from an automated system, targeting novice users in a domain-general context. As a proof of concept, we demonstrate the practical efficacy and feasibility of this approach in a user study. Our results suggest that users benefit from receiving CFEs compared to no explanation, both in terms of objective performance in the proposed iterative learning task, and subjective usability. With this work, we aim to equip research groups and practitioners with the means to easily run controlled and well-powered user studies to complement their otherwise often more technology-oriented work. Thus, in the interest of reproducible research, we provide the entire code, together with the underlying models and user data.

This survey article assesses and compares existing critiques of current fairness-enhancing technical interventions in machine learning (ML) that draw from a range of non-computing disciplines, including philosophy, feminist studies, critical race and ethnic studies, legal studies, anthropology, and science and technology studies. It bridges epistemic divides in order to offer an interdisciplinary understanding of the possibilities and limits of hegemonic computational approaches to ML fairness for producing just outcomes for society's most marginalized. The article is organized according to nine major themes of critique wherein these different fields intersect: 1) how "fairness" in AI fairness research gets defined; 2) how problems for AI systems to address get formulated; 3) the impacts of abstraction on how AI tools function and its propensity to lead to technological solutionism; 4) how racial classification operates within AI fairness research; 5) the use of AI fairness measures to avoid regulation and engage in ethics washing; 6) an absence of participatory design and democratic deliberation in AI fairness considerations; 7) data collection practices that entrench "bias," are non-consensual, and lack transparency; 8) the predatory inclusion of marginalized groups into AI systems; and 9) a lack of engagement with AI's long-term social and ethical outcomes. Drawing from these critiques, the article concludes by imagining future ML fairness research directions that actively disrupt entrenched power dynamics and structural injustices in society.

The past few years have brought much hand wringing and arm waving about artificial intelligence (AI), as business people and technologists alike worry about the outsize decisioning power they believe these systems to have. As a data scientist, I am accustomed to being the voice of reason about the possibilities and limitations of AI. In this article I'll explain how companies can use blockchain technology for model development governance, a breakthrough to better understand AI, make the model development process auditable, and identify and assign accountability for AI decisioning. While there is widespread awareness about the need to govern AI, the discussion about how to do so is often nebulous, such as in "How to Build Accountability into Your AI" in Harvard Business Review: A healthy ecosystem for managing AI must include governance processes and structures.... Accountability for AI means looking for solid evidence of governance at the organizational level, including clear goals and objectives for the AI system; well-defined roles, responsibilities, and lines of authority; a multidisciplinary workforce capable of managing AI systems; a broad set of stakeholders; and risk-management processes. Additionally, it is vital to look for system-level governance elements, such as documented technical specifications of the particular AI system, compliance, and stakeholder access to system design and operation information.

While artificial intelligence (AI) technology has the potential to transform society, the legal issues it raises touch upon diverse areas of law. These areas include privacy and data security, commercial contracts, intellectual property, antitrust, employee benefits, and products liability. AI is broadly defined as computer technology that can simulate human intelligence. Through algorithms, this software can aggregate data, detect patterns, optimize behaviors, and make future predictions. Some examples of AI applications include machine learning, natural language processing, artificial neural networks, machine perception, and motion manipulation.

In a live interview with the Washington Post last week, New York-based Clearview AI's co-founder and CEO Hoan Ton-That addressed questions about the ethical and legal implications of his software, which became first known to many Americans when a billionaire used it to identify his daughter's dinner date, and for the involvement of far-right individuals in the creation of the company. Pressed on questions about the legal and ethical choices his firm has made while creating a searchable database of 20 billion facial images, Ton-That repeatedly brought up examples where the use cases of Clearview AI's technology would look better in the public eye, mentioning its use in helping catch criminals in child pornography and child abuse cases. Ton-That also pointed to the use of Clearview AI's technology by the Ukrainian government to identify dead Russian soldiers, for notifying their families of their passing.

I hope that you enjoy the latest AI news and insights, don't forget to comment with your feedback. From this week you can find some interesting stuff added to the last section. But they have had a hard time shaking infighting and controversy over a variety of issues. Biased datasets are often the source for why AI models are also biased. "Adoption and scaling aren't things you add at the tail end of a project; they're where you need to start," Join 6000 aspiring Data Scientists to watch this FREE 75-minute session.