Hate speech recognizers (HSRs) can be the panacea for containing hate in social media or can result in the biggest form of prejudice-based censorship hindering people to express their true selves. In this paper, we hypothesized how massive use of syntax can reduce the prejudice effect in HSRs. To explore this hypothesis, we propose Unintended-bias Visualizer based on Kermit modeling (KERM-HATE): a syntax-based HSR, which is endowed with syntax heat parse trees used as a post-hoc explanation of classifications. KERM-HATE significantly outperforms BERT-based, RoBERTa-based and XLNet-based HSR on standard datasets. Surprisingly this result is not sufficient. In fact, the post-hoc analysis on novel datasets on recent divisive topics shows that even KERM-HATE carries the prejudice distilled from the initial corpus. Therefore, although tests on standard datasets may show higher performance, syntax alone cannot drive the “attention” of HSRs to ethically-unbiased features.

AltaML was founded in 2018, employs around 130 employees and has offices in Edmonton, Calgary and Toronto. The company works in industries such as oil and gas, banking, forestry, agriculture and health. "We work with them to uncover those possibilities for the application of machine learning," says Rabelo. "When we identify those opportunities, we develop studies – basically, experiments – to see if our hypotheses really hold true when we apply them to real world data." "As we validate those hypotheses, those opportunities move on a chain and eventually they reach solution phase, where they are deployed to production. They are developed as part of software system or an [application programming interface] or something that can be directly deployed to those industries, to those clients."

The impact of Artificial Intelligence does not depend only on fundamental research and technological developments, but for a large part on how these systems are introduced into society and used in everyday situations. Even though AI is traditionally associated with rational decision making, understanding and shaping the societal impact of AI in all its facets requires a relational perspective. A rational approach to AI, where computational algorithms drive decision making independent of human intervention, insights and emotions, has shown to result in bias and exclusion, laying bare societal vulnerabilities and insecurities. A relational approach, that focus on the relational nature of things, is needed to deal with the ethical, legal, societal, cultural, and environmental implications of AI. A relational approach to AI recognises that objective and rational reasoning cannot does not always result in the 'right' way to proceed because what is 'right' depends on the dynamics of the situation in which the decision is taken, and that rather than solving ethical problems the focus of design and use of AI must be on asking the ethical question. In this position paper, I start with a general discussion of current conceptualisations of AI followed by an overview of existing approaches to governance and responsible development and use of AI. Then, I reflect over what should be the bases of a social paradigm for AI and how this should be embedded in relational, feminist and non-Western philosophies, in particular the Ubuntu philosophy.

For Glovo, being a fast growing startup has meant that for years we have prioritized productionizing ML models over engaging frequently with the research community. In the last year, however, we felt like we reached the maturity stage needed to start engaging with the rest of the AI community. To help with this, we founded a new team within our Central Data Science organization: the CORE (Collaboration and Research) team. CORE's mission is to explore high-risk high-reward R&D projects in the AI space, foster scientific publications and increase conference attendance. A key part of this initiative is sponsoring and participating in relevant AI conferences.

Democrats in Congress on Thursday renewed a push to hold tech companies accountable for bias in their algorithms. Senators Ron Wyden (D-OR) and Cory Booker (D-NJ), along with House representative Yvette Clarke (D-NY) introduced an updated version of a bill that would require audits of AI systems used in areas such as finance, healthcare, housing, education and more. First introduced by Wyden in 2019, the Algorithmic Accountability Act has never passed the committee level in either the House or Senate. "If someone decides not to rent you a house because of the color of your skin, that's flat-out illegal discrimination. Using a flawed algorithm or software that results in discrimination and bias is just as bad. Our bill will pull back the curtain on the secret algorithms that can decide whether Americans get to see a doctor, rent a house or get into a school," said Wyden in a press release.

In selection processes such as hiring, promotion, and college admissions, implicit bias toward socially-salient attributes such as race, gender, or sexual orientation of candidates is known to produce persistent inequality and reduce aggregate utility for the decision maker. Interventions such as the Rooney Rule and its generalizations, which require the decision maker to select at least a specified number of individuals from each affected group, have been proposed to mitigate the adverse effects of implicit bias in selection. Recent works have established that such lower-bound constraints can be very effective in improving aggregate utility in the case when each individual belongs to at most one affected group. However, in several settings, individuals may belong to multiple affected groups and, consequently, face more extreme implicit bias due to this intersectionality. We consider independently drawn utilities and show that, in the intersectional case, the aforementioned non-intersectional constraints can only recover part of the total utility achievable in the absence of implicit bias. On the other hand, we show that if one includes appropriate lower-bound constraints on the intersections, almost all the utility achievable in the absence of implicit bias can be recovered. Thus, intersectional constraints can offer a significant advantage over a reductionist dimension-by-dimension non-intersectional approach to reducing inequality.

The "race" for dominance in AI is far from fair: Not only do a few developed economies possess certain material advantages right from the start, they also set the rules. They have an advantage in research and development, and possess a skilled workforce as well as wealth to invest in AI. We can also look at the state of inequity in AI in terms of governance: How "tech fluent" are policymakers in developing and underdeveloped countries? What barriers do they face in crafting regulations and industrial policy? Are they sufficiently represented and empowered at the international bodies that set rules and standards on AI? At the same time, there is an emerging challenge at the nexus of AI and climate change that could deepen this inequity.

Alexa AI is co-organizing three special sessions -- themed sessions within the main conferences -- at two major 2022 conferences on speech-related technologies, and two of those sessions are currently seeking paper submissions. At the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), in May, Alexa AI is co-organizing a special session on federated learning, a machine learning paradigm in which distributed computers process privacy-protected data locally, but the results of the distributed computations are amalgamated into a single joint model. At Interspeech, in September, Alexa AI is co-organizing two special sessions, and both are still open for submissions. One session is on machine learning and signal processing in the context of multiple networked smart devices. This session will address topics such as synchronization, arbitration (deciding which device should respond to a query), and privacy.

Arange of regulatory changes and new hires from the Biden administration signals a more proactive stance by the federal government towards artificial intelligence (AI) regulation, which brings the U.S. closer to that of the European Union (EU). These developments are promising, as is the inclusion of AI issues in the new EU-U.S. Trade and Technology Council (TTC). But there are other steps that these leading democracies can take to build alignment on curtailing AI harms. Since 2017, at least 60 countries have adopted some form of artificial intelligence policy, a torrent of activity that nearly matches the pace of modern AI adoption. The expansion of AI governance raises concerns about looming challenges for international cooperation.

More and more people have started to pay attention to artificial intelligence (AI) in recent years. According to Edelman's special report on tech within its annual Trust Barometer report, people around the world have shown concern that AI and robots could replace human workers. As a result, fewer people are willing to share their personal data, as their trust in the media, online social platforms and search engines seems to have declined. Some say the chasm between trust and technology has formed for good reasons: For most of AI's existence, there hasn't been much regulation around it. At times, the rules may seem a bit loose and opaque for just how world-changing it could be.