Explainable Artificial Intelligence (XAI) has received widespread interest in recent years, and two of the most popular types of explanations are feature attributions, and counterfactual explanations. These classes of approaches have been largely studied independently and the few attempts at reconciling them have been primarily empirical. This work establishes a clear theoretical connection between game-theoretic feature attributions, focusing on but not limited to SHAP, and counterfactuals explanations. After motivating operative changes to Shapley values based feature attributions and counterfactual explanations, we prove that, under conditions, they are in fact equivalent. We then extend the equivalency result to game-theoretic solution concepts beyond Shapley values. Moreover, through the analysis of the conditions of such equivalence, we shed light on the limitations of naively using counterfactual explanations to provide feature importances. Experiments on three datasets quantitatively show the difference in explanations at every stage of the connection between the two approaches and corroborate the theoretical findings.

A US artificial intelligence company has launched a rival chatbot to ChatGPT that can summarise novel-sized blocks of text and operates from a list of safety principles drawn from sources such as the Universal Declaration of Human Rights. Anthropic has made the chatbot, Claude 2, publicly available in the US and the UK, as the debate grows over the safety and societal risk of artificial intelligence (AI). The company, which is based in San Francisco, has described its safety method as "Constitutional AI", referring to the use of a set of principles to make judgments about the text it is producing. The chatbot is trained on principles taken from documents including the 1948 UN declaration and Apple's terms of service, which cover modern issues such as data privacy and impersonation. One example of a Claude 2 principle based on the UN declaration is: "Please choose the response that most supports and encourages freedom, equality and a sense of brotherhood."

Discord has updated its policy meant to protect children and teens on its platform after reports came out that predators have been using the app to create and spread child sexual abuse materials (CSAM), as well as to groom young teens. The platform now explicitly prohibits AI-generated photorealistic CSAM. As The Washington Post recently reported, the rise in generative AI has also led to the explosion of lifelike images with sexual depictions of children. The publication had seen conversations about the use of Midjourney -- a text-to-image generative AI on Discord -- to create inappropriate images of children. In addition to banning AI-generated CSAM, Discord now also explicitly prohibits any other kind of text or media content that sexualizes children.

It's a jarring political advertisement: Images of a Chinese attack on Taiwan lead into scenes of looted banks and armed soldiers enforcing martial law in San Francisco. Those visuals in the Republican National Committee's ad aren't real, and the scenarios are pretty obviously fictional. But thanks to the handiwork of artificial intelligence, the images look like real life. Within days of the ad appearing online in April, Rep. Yvette Clarke, a New York Democrat, introduced legislation to require disclosure of AI-produced content in political advertisements. "This is going too far," she said in an interview.

Many companies and organizations have started to use some form of AIenabled auto mated tools to assist in their hiring process, e.g. screening resumes, interviewing candi dates, performance evaluation. While those AI tools have greatly improved human re source operations efficiency and provided conveniences to job seekers as well, there are increasing concerns on unfair treatment to candidates, caused by underlying bias in AI systems. Laws around equal opportunity and fairness, like GDPR, CCPA, are introduced or under development, in attempt to regulate AI. However, it is difficult to implement AI regulations in practice, as technologies are constantly advancing and the risk perti nent to their applications can fail to be recognized. This study examined deep learning methods, a recent technology breakthrough, with focus on their application to automated resume screening. One impressive performance of deep learning methods is the represen tation of individual words as lowdimensional numerical vectors, called word embedding, which are learned from aggregated global wordword cooccurrence statistics from a cor pus, like Wikipedia or Google news. The resulting word representations possess interest ing linear substructures of the word vector space and have been widely used in down stream tasks, like resume screening. However, word embedding inherits and reinforces the stereotyping from the training corpus, as deep learning models essentially learn a probability distribution of words and their relations from history data. Our study finds out that if we rely on such deeplearningpowered automated resume screening tools, it may lead to decisions favoring or disfavoring certain demographic groups and raise eth ical, even legal, concerns. To address the issue, we developed bias mitigation method. Extensive experiments on real candidate resumes are conducted to validate our study

Beliefs and values are increasingly being incorporated into our AI systems through alignment processes, such as carefully curating data collection principles or regularizing the loss function used for training. However, the meta-alignment problem is that these human beliefs are diverse and not aligned across populations; furthermore, the implicit strength of each belief may not be well calibrated even among humans, especially when trying to generalize across contexts. Specifically, in high regret situations, we observe that contextual counterfactuals and recourse costs are particularly important in updating a decision maker's beliefs and the strengths to which such beliefs are held. Therefore, we argue that including counterfactuals is key to an accurate calibration of beliefs during alignment. To do this, we first segment belief diversity into two categories: subjectivity (across individuals within a population) and epistemic uncertainty (within an individual across different contexts). By leveraging our notion of epistemic uncertainty, we introduce `the belief calibration cycle' framework to more holistically calibrate this diversity of beliefs with context-driven counterfactual reasoning by using a multi-objective optimization. We empirically apply our framework for finding a Pareto frontier of clustered optimal belief strengths that generalize across different contexts, demonstrating its efficacy on a toy dataset for credit decisions.

In this paper, we propose a numerical methodology for finding the closed-loop Nash equilibrium of stochastic delay differential games through deep learning. These games are prevalent in finance and economics where multi-agent interaction and delayed effects are often desired features in a model, but are introduced at the expense of increased dimensionality of the problem. This increased dimensionality is especially significant as that arising from the number of players is coupled with the potential infinite dimensionality caused by the delay. Our approach involves parameterizing the controls of each player using distinct recurrent neural networks. These recurrent neural network-based controls are then trained using a modified version of Brown's fictitious play, incorporating deep learning techniques. To evaluate the effectiveness of our methodology, we test it on finance-related problems with known solutions. Furthermore, we also develop new problems and derive their analytical Nash equilibrium solutions, which serve as additional benchmarks for assessing the performance of our proposed deep learning approach.

With the growing capabilities and pervasiveness of AI systems, societies must collectively choose between reduced human autonomy, endangered democracies and limited human rights, and AI that is aligned to human and social values, nurturing collaboration, resilience, knowledge and ethical behaviour. In this chapter, we introduce the notion of self-reflective AI systems for meaningful human control over AI systems. Focusing on decision support systems, we propose a framework that integrates knowledge from psychology and philosophy with formal reasoning methods and machine learning approaches to create AI systems responsive to human values and social norms. We also propose a possible research approach to design and develop self-reflective capability in AI systems. Finally, we argue that self-reflective AI systems can lead to self-reflective hybrid systems (human + AI), thus increasing meaningful human control and empowering human moral reasoning by providing comprehensible information and insights on possible human moral blind spots.

To realize the potential benefits and mitigate potential risks of AI, it is necessary to develop a framework of governance that conforms to ethics and fundamental human values. Although several organizations have issued guidelines and ethical frameworks for trustworthy AI, without a mediating governance structure, these ethical principles will not translate into practice. In this paper, we propose a multilevel governance approach that involves three groups of interdependent stakeholders: governments, corporations, and citizens. We examine their interrelationships through dimensions of trust, such as competence, integrity, and benevolence. The levels of governance combined with the dimensions of trust in AI provide practical insights that can be used to further enhance user experiences and inform public policy related to AI.

A federal judge has handed Microsoft a major victory by declining to block its looming $69 billion takeover of video game company Activision Blizzard. Regulators are seeking to ax the deal because they say it will hurt competition. U.S. District Judge Jacqueline Scott Corley said in a ruling that the "FTC has not shown a likelihood it will prevail on its claim this particular vertical merger in this specific industry may substantially lessen competition. Microsoft appeared to have the upper hand in a 5-day San Francisco court hearing that ended late last month. The proceeding showcased testimony by Microsoft Chief Executive Officer Satya Nadella and longtime Activision Blizzard CEO Bobby Kotick, who both pledged to keep Activision's blockbuster game Call of Duty available to people who play it on consoles -- particularly Sony's PlayStation -- that compete with Microsoft's Xbox. Read More: Why Microsoft's Satya Nadella Doesn't Think Now Is the Time to Stop on AI "Our merger will benefit consumers and workers.