This paper proposes two intuitive metrics, skew and stereotype, that quantify and analyse the gender bias present in contextual language models when tackling the WinoBias pronoun resolution task. We find evidence that gender stereotype correlates approximately negatively with gender skew in out-of-the-box models, suggesting that there is a trade-off between these two forms of bias. We investigate two methods to mitigate bias. The first approach is an online method which is effective at removing skew at the expense of stereotype. The second, inspired by previous work on ELMo, involves the fine-tuning of BERT using an augmented gender-balanced dataset. We show that this reduces both skew and stereotype relative to its unaugmented fine-tuned counterpart. However, we find that existing gender bias benchmarks do not fully probe professional bias as pronoun resolution may be obfuscated by cross-correlations from other manifestations of gender prejudice. Our code is available online, at https://github.com/12kleingordon34/NLP_masters_project.

Previous research in Explainable Artificial Intelligence (XAI) suggests that a main aim of explainability approaches is to satisfy specific interests, goals, expectations, needs, and demands regarding artificial systems (we call these stakeholders' desiderata) in a variety of contexts. However, the literature on XAI is vast, spreads out across multiple largely disconnected disciplines, and it often remains unclear how explainability approaches are supposed to achieve the goal of satisfying stakeholders' desiderata. This paper discusses the main classes of stakeholders calling for explainability of artificial systems and reviews their desiderata. We provide a model that explicitly spells out the main concepts and relations necessary to consider and investigate when evaluating, adjusting, choosing, and developing explainability approaches that aim to satisfy stakeholders' desiderata. This model can serve researchers from the variety of different disciplines involved in XAI as a common ground. It emphasizes where there is interdisciplinary potential in the evaluation and the development of explainability approaches.

In this chapter we argue that discourses on AI must transcend the language of 'ethics' and engage with power and political economy in order to constitute 'Good Data'. In particular, we must move beyond the depoliticised language of 'ethics' currently deployed (Wagner 2018) in determining whether AI is 'good' given the limitations of ethics as a frame through which AI issues can be viewed. In order to circumvent these limits, we use instead the language and conceptualisation of 'Good Data', as a more expansive term to elucidate the values, rights and interests at stake when it comes to AI's development and deployment, as well as that of other digital technologies. Good Data considerations move beyond recurring themes of data protection/privacy and the FAT (fairness, transparency and accountability) movement to include explicit political economy critiques of power. Instead of yet more ethics principles (that tend to say the same or similar things anyway), we offer four 'pillars' on which Good Data AI can be built: community, rights, usability and politics. Overall we view AI's 'goodness' as an explicly political (economy) question of power and one which is always related to the degree which AI is created and used to increase the wellbeing of society and especially to increase the power of the most marginalized and disenfranchised. We offer recommendations and remedies towards implementing 'better' approaches towards AI. Our strategies enable a different (but complementary) kind of evaluation of AI as part of the broader socio-technical systems in which AI is built and deployed.

As the potential applications of Artificial Intelligence (AI) in the financial sector increases, ethical issues become gradually latent. The distrust of individuals, social groups, and governments about the risks arising from Fintech's activities is growing. Due to this scenario, the preparation of recommendations and Ethics Guidelines is increasing and the risks of being chosen the principles and ethical values most appropriate to companies are high. Thus, this exploratory research aims to analyze the benefits of the application of the stakeholder theory and the idea of Social License to build an environment of trust and for the realization of ethical principles by Fintech. The formation of a Fintech association for the creation of a Social License will allow early-stage Fintech to participate from the beginning of its activities in the elaboration of a dynamic ethical code and with the participation of stakeholders.

So as the United Nations, European authorities and human-rights groups build war-crimes cases, they have turned to a novel tool: artificial intelligence. With the regime of President Bashar al-Assad emerging largely victorious from nearly a decade of conflict, efforts to bring about some measure of accountability are gaining speed, largely in European courts. Since the beginning of Syria's conflict, activists on the ground risked their lives to document human-rights violations, from torture and attacks on protesters to indiscriminate rocket strikes and barrel bombs. Now, AI and machine learning could play an integral role in bringing war criminals to justice for Syria by helping to sort through the huge trove of evidence, and serve as a model for investigations into other modern-day conflicts. "You have a use of technology both to disseminate the information, capture it, and now to search it that is suddenly very different and changes the way you work," said Catherine Marchi-Uhel, who heads the United Nations body tasked with collecting Syrian evidence and building cases.

As Valentine's Day approaches, couples across the country are preparing for this long-standing tradition--and there's a very good chance they met through online dating. But while dating apps can help people find a partner (or just a fun date), they can also subject users to incredible hate and harassment. Despite the fact that dating apps have accrued significant reach and influence, these companies provide very little transparency around how they keep users safe and how they moderate content. Much of the conversation around online platform accountability focuses on companies like Facebook and Google. But dating apps face many of the same issues.

Building the level required a lot of "experimentation," lead and senior level designer Toke Krainert told The Washington Post. Once they settled on a manor -- a decision made fairly early -- the next question was gameplay and story. They wanted to provide insight into Providence, as Alexa Carlisle would be the main target. A murder mystery felt like the perfect way to do that, giving players backstory on the Carlisle family as they uncovered clues and interrogated suspects inside the mansion.

In January, when New York's Public Oversight of Surveillance Technology Act went into effect, the City of New York Police Department was suddenly forced to detail the tools it had long kept from public view. But instead of giving New Yorkers transparency, the NYPD gave error-filled, boilerplate statements that hide almost everything of value. Almost none of the policies list specific vendors, surveillance tool models, or information-sharing practices. The department's facial recognition policy says it can share data "pursuant to on-going criminal investigations, civil litigation, and disciplinary proceedings," a standard so broad it's largely meaningless. This marks the greatest test yet of Community Control of Police Surveillance (CCOPS), a growing effort to ensure that the public can take back control over the decisions of how communities are surveilled, deciding whether tools like facial recognition, drones, and predictive policing are acceptable for their neighborhoods.

Semantic web technologies have shown their effectiveness, especially when it comes to knowledge representation, reasoning, and data integrations. However, the original semantic web vision, whereby machine readable web data could be automatically actioned upon by intelligent software web agents, has yet to be realised. In order to better understand the existing technological challenges and opportunities, in this paper we examine the status quo in terms of intelligent software web agents, guided by research with respect to requirements and architectural components, coming from that agents community. We start by collating and summarising requirements and core architectural components relating to intelligent software agent. Following on from this, we use the identified requirements to both further elaborate on the semantic web agent motivating use case scenario, and to summarise different perspectives on the requirements when it comes to semantic web agent literature. Finally, we propose a hybrid semantic web agent architecture, discuss the role played by existing semantic web standards, and point to existing work in the broader semantic web community any beyond that could help us to make the semantic web agent vision a reality.

For AI technology to fulfill its full promises, we must design effective mechanisms into the AI systems to support responsible AI behavior and curtail potential irresponsible use, e.g. in areas of privacy protection, human autonomy, robustness, and prevention of biases and discrimination in automated decision making. In this paper, we present a framework that provides computational facilities for parties in a social ecosystem to produce the desired responsible AI behaviors. To achieve this goal, we analyze AI systems at the architecture level and propose two decentralized cryptographic mechanisms for an AI system architecture: (1) using Autonomous Identity to empower human users, and (2) automating rules and adopting conventions within social institutions. We then propose a decentralized approach and outline the key concepts and mechanisms based on Decentralized Identifier (DID) and Verifiable Credentials (VC) for a general-purpose computational infrastructure to realize these mechanisms. We argue the case that a decentralized approach is the most promising path towards Responsible AI from both the computer science and social science perspectives.