Privacy restrictions are pushing many marketer toward the use of artificial intelligence in order to ... [ ] delive more targeted messages. The trend toward greater focus on privacy issues has been going on for some time and is starting to come to a head. More restrictions on the sharing and merging of data on individuals has been leading to advertisers to look for effective ways to target and reach consumers, including using the use of behavioral targeting supplemented by the use of artificial intelligence (AI). At a time when privacy regulations are sometimes fragmented and confusing but changing, it is critically important for marketers to monitor changes in the regulatory environment. Against this backdrop, I interviewed Sheri Bachstein, IBM's Global Head of Watson Advertising to get her insights and predictions on the future of privacy regulation and how it will affect advertisers, particularly as regards the use of AI and came away with three major takeaways: The European Union's General Data Protection Regulation and the California Consumer Privacy Act are already leading to the devaluation of traditional third-party cookies and the way many advertisers do business.

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The combined technology – OneTrust Data Redaction – is available today and helps privacy, legal, and information security teams find, redact, and protect sensitive and personal information in documents and emails. OneTrust Data Redaction, integrated into the OneTrust privacy, security, and data governance platform, completes the first fully automated data subject rights (DSAR) workflow including intake, ID verification, discovery, redaction, and secure response. Many of the world's privacy laws give individuals the right to make requests about their data, such as the right to access under the GDPR and CCPA. Organizations must redact other's personal information and sensitive corporate information before providing the requested information to the requestor. The combination of OneTrust Data Redaction and OneTrust's DSAR Automation technology integrates advanced data redaction to fully automate the DSAR process with deep data discovery, redaction, ID verification, and secure communication technologies.

Contrastive explanations clarify why an event occurred in contrast to another. They are more inherently intuitive to humans to both produce and comprehend. We propose a methodology to produce contrastive explanations for classification models by modifying the representation to disregard non-contrastive information, and modifying model behavior to only be based on contrastive reasoning. Our method is based on projecting model representation to a latent space that captures only the features that are useful (to the model) to differentiate two potential decisions. We demonstrate the value of contrastive explanations by analyzing two different scenarios, using both high-level abstract concept attribution and low-level input token/span attribution, on two widely used text classification tasks. Specifically, we produce explanations for answering: for which label, and against which alternative label, is some aspect of the input useful? And which aspects of the input are useful for and against particular decisions? Overall, our findings shed light on the ability of label-contrastive explanations to provide a more accurate and finer-grained interpretability of a model's decision.

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In recent years, AI has evolved from science fiction to part of our everyday lives. Emerging tech is on the cusp of revolutionizing global value chains. Shaping a "new corporate tomorrow" is already taking place, and the ethical issues related to AI have been laid out in numerous executive debates. To secure democratic values and a high standard of transparency, we must take AI Ethics to the next level: AI Policy. History shows that policy and lawmaking do the trick when it comes to protecting liberty, democratic values, and human rights.

A concise but informative overview of AI ethics and policy. Artificial intelligence, or AI for short, has generated a staggering amount of hype in the past several years. Is it the game-changer it's been cracked up to be? If so, how is it changing the game? How is it likely to affect us as customers, tenants, aspiring homeowners, students, educators, patients, clients, prison inmates, members of ethnic and sexual minorities, and voters in liberal democracies? Authored by experts in fields ranging from computer science and law to philosophy and cognitive science, this book offers a concise overview of moral, political, legal and economic implications of AI. It covers the basics of AI's latest permutation, machine learning, and considers issues such as transparency, bias, liability, privacy, and regulation.Both business and government have integrated algorithmic decision support systems into their daily operations, and the book explores the implications for our lives as citizens. For example, do we take it on faith that a machine knows best in approving a patient's health insurance claim or a defendant's request for bail? What is the potential for manipulation by targeted political ads? How can the processes behind these technically sophisticated tools ever be transparent? The book discusses such issues as statistical definitions of fairness, legal and moral responsibility, the role of humans in machine learning decision systems, “nudging” algorithms and anonymized data, the effect of automation on the workplace, and AI as both regulatory tool and target.

REUTERS: Alphabet Inc's Google will change procedures before July for reviewing its scientists' work, according to a town hall recording heard by Reuters, part of an effort to quell internal tumult over the integrity of its artificial intelligence (AI) research. In remarks at a staff meeting last Friday, Google Research executives said they were working to regain trust after the company ousted two prominent women and rejected their work, according to an hour-long recording, the content of which was confirmed by two sources. Teams are already trialing a questionnaire that will assess projects for risk and help scientists navigate reviews, research unit Chief Operating Officer Maggie Johnson said in the meeting. This initial change will roll out by the end of the second quarter, and the majority of papers will not require extra vetting, she said. Reuters reported in December that Google had introduced a "sensitive topics" review for studies involving dozens of issues, such as China or bias in its services.

Timnit Gebru--a giant in the world of AI and then co-lead of Google's AI ethics team--was pushed out of her job in December. Gebru had been fighting with the company over a research paper that she'd coauthored, which explored the risks of the AI models that the search giant uses to power its core products--the models are involved in almost every English query on Google, for instance. The paper called out the potential biases (racial, gender, Western, and more) of these language models, as well as the outsize carbon emissions required to compute them. Google wanted the paper retracted, or any Google-affiliated authors' names taken off; Gebru said she would do so if Google would engage in a conversation about the decision. Instead, her team was told that she had resigned. After the company abruptly announced Gebru's departure, Google AI chief Jeff Dean insinuated that her work was not up to snuff--despite Gebru's credentials and history of groundbreaking research.

An important step in the development of value alignment (VA) systems in artificial intelligence (AI) is understanding how VA can reflect valid ethical principles. We propose that designers of VA systems incorporate ethics by utilizing a hybrid approach in which both ethical reasoning and empirical observation play a role. This, we argue, avoids committing “naturalistic fallacy,” which is an attempt to derive “ought” from “is,” and it provides a more adequate form of ethical reasoning when the fallacy is not committed. Using quantified modal logic, we precisely formulate principles derived from deontological ethics and show how they imply particular “test propositions” for any given action plan in an AI rule base. The action plan is ethical only if the test proposition is empirically true, a judgment that is made on the basis of empirical VA. This permits empirical VA to integrate seamlessly with independently justified ethical principles. This article is part of the special track on AI and Society.