But the European Commission's draft legislation on artificial intelligence, published in April, is anything but luxury. It will apply to any AI system whose recommendation can influence an EU citizen, be that a customer or an employee. Compliance could be complex -- and costly.

On October 28, 2021, the U.S. Equal Employment Opportunity Commission (EEOC) announced the launching of an initiative to ensure that Artificial Intelligence (AI) and other emerging tools used in hiring and other employment decisions comply with federal civil rights laws that the agency enforces, according to an EEOC press release. "Artificial intelligence and algorithmic decision-making tools have great potential to improve our lives, including in the area of employment. At the same time, the EEOC is keenly aware that these tools may mask and perpetuate bias or create new discriminatory barriers to jobs," EEOC Chair Charlotte A. Burrows stated in the press release. The initiative will examine how technology is fundamentally changing the way employment decisions are made and aims to guide applicants, employees, employers, and technology vendors in ensuring these technologies are used with federal equal employment opportunity (EEO) laws. "Bias in employment arising from the use of algorithms and AI falls squarely within the Commission's priority to address systemic discrimination. The EEOC will address workplace bias that violates federal civil rights laws regardless of the form it takes," said Burrows, who was appointed by President Joe Biden in January 2021.

Ask Delphi is not a physical robot with a solid body. It's actually just a software that was supposed to let users ask tricky questions that do …

In this paper, we argue that AI ethics must move beyond the concepts of race-based representation and bias, and towards those that probe the deeper relations that impact how these systems are designed, developed, and deployed. Many recent discussions on ethical considerations of bias in AI systems have centered on racial bias. We contend that antiblackness in AI requires more of an examination of the ontological space that provides a foundation for the design, development, and deployment of AI systems. We examine what this contention means from the perspective of the sociocultural context in which AI systems are designed, developed, and deployed and focus on intersections with anti-Black racism (antiblackness). To bring these multiple perspectives together and show an example of antiblackness in the face of attempts at de-biasing, we discuss results from auditing an existing open-source semantic network (ConceptNet). We use this discussion to further contextualize antiblackness in design, development, and deployment of AI systems and suggest questions one may ask when attempting to combat antiblackness in AI systems.

The use of automated decision support and decision-making systems (ADM) (Hardt, Price, and Srebro 2016) in applications with direct impact on people's lives has increasingly become a fact of life, e,g. in criminal justice (Kleinberg, Contributions. We propose a technique that uses Bias Mullainathan, and Raghavan 2016; Jain et al. 2020b; Dressel Parity Score (BPS) measures to characterize fairness and develop and Farid 2018), medical diagnosis (Kleinberg, Mullainathan, a family of corresponding loss functions that are used and Raghavan 2016; Ahsen, Ayvaci, and Raghunathan as regularizers during training of Neural Networks to enhance 2019), insurance (Baudry and Robert 2019), credit fairness of the trained models. The goal here is to permit card fraud detection (Dal Pozzolo et al. 2014), electronic the system to actively pursue fair solutions during training health record data (Gianfrancesco et al. 2018), credit scoring while maintaining as high a performance on the task as (Huang, Chen, and Wang 2007) and many more diverse possible. We apply the approach in the context of several domains. This, in turn, has lead to an urgent need fairness measures and investigate multiple loss function formulations for study and scrutiny of the bias-magnifying effects of machine and regularization weights in order to study the learning and Artificial Intelligence algorithms and thus performance as well as potential drawbacks and deployment their potential to introduce and emphasize social inequalities considerations. In these experiments we show that, if used and systematic discrimination in our society. Appropriately, with appropriate settings, the technique measurably reduces much research is being done currently to mitigate bias race-based bias in recidivism prediction, and demonstrate in AI-based decision support systems (Ahsen, Ayvaci, and on the gender-based Adult Income dataset that the proposed Raghunathan 2019; Kleinberg, Mullainathan, and Raghavan method can outperform state-of-the art techniques aimed at 2016; Noriega-Campero et al. 2019; Feldman 2015; more targeted aspects of bias and fairness.

Dialogue summarization has been extensively studied and applied, where the prior works mainly focused on exploring superior model structures to align the input dialogue and the output summary. However, for professional dialogues (e.g., legal debate and medical diagnosis), semantic/statistical alignment can hardly fill the logical/factual gap between input dialogue discourse and summary output with external knowledge. In this paper, we mainly investigate the factual inconsistency problem for Dialogue Inspectional Summarization (DIS) under non-pretraining and pretraining settings. An innovative end-to-end dialogue summary generation framework is proposed with two auxiliary tasks: Expectant Factual Aspect Regularization (EFAR) and Missing Factual Entity Discrimination (MFED). Comprehensive experiments demonstrate that the proposed model can generate a more readable summary with accurate coverage of factual aspects as well as informing the user with potential missing facts detected from the input dialogue for further human intervention.

A firm which claims to have a database of more than 10 billion facial images has breached Australia's privacy laws, Australian regulators say. Clearview AI lets law enforcement agencies search its database of faces. But the Office of the Australian Information Commissioner (OAIC) ordered it to stop collecting photos taken in Australia and remove ones already in its collection. A lawyer representing the firm said it would seek a review of the decision. Clearview AI's system allows a user – for example, a police officer seeking to identify a suspect – to upload a photo of a face and find matches in a database of billions of images it has collected from the internet and social media.

After Canada, now Australia has found that controversial facial recognition company, Clearview AI, broke national privacy laws when it covertly collected citizens' facial biometrics and incorporated them into its AI-powered identity matching service -- which it sells to law enforcement agencies and others. In a statement today, Australia's information commissioner and privacy commissioner, Angelene Falk, said Clearview AI's facial recognition tool breached the country's Privacy Act 1988 by: In what looks like a major win for privacy down under, the regulator has ordered Clearview to stop collecting facial biometrics and biometric templates from Australians; and to destroy all existing images and templates that it holds. The Office of the Australian Information Commissioner (OAIC) undertook a joint investigation into Clearview with the UK data protection agency, the Information Commission's Office (IOC). However the UK regulator has yet to announce any conclusions. In a separate statement today -- which possibly reads slightly flustered -- the ICO said it is "considering its next steps and any formal regulatory action that may be appropriate under the UK data protection laws".

However, when Dazed tested it using country names, it described the UK and US as "good", France as "nice", and Russia as "a great place to visit", but said Nigeria, Mexico, and Iraq were "dangerous", while Iran was "bad". Clearly, the software – like much artificial intelligence – has a problem with racism. Its creators have addressed this in a post-launch Q&A, writing: "Today's society is unequal and biased. This is a common issue with AI systems, as many scholars have argued, because AI systems are trained on historical or present data and have no way of shaping the future of society, only humans can. What AI systems like Delphi can do, however, is learn about what is currently wrong, socially unacceptable, or biased, and be used in conjunction with other, more problematic, AI systems (to) help avoid that problematic content."

US activist raises concerns over racial bias in AI Urges software developers to listen more to Black people Wrongful arrest in US caused by faulty facial recognition Lisbon playing host to Europe's largest tech event LISBON, Nov 3 (Reuters) - (This Nov. 3 story corrects to change BLM co-founder first name to Ayo from Opal at her request) As concerns grow over racial bias in artificial intelligence, Black Lives Matter (BLM) co-founder Ayo Tometi urged the tech sector to act fast against perpetuating racism in systems such as facial recognition. Artificial intelligence is transforming the world and can be applied in diverse sectors, from improving the early detection of diseases to sorting out data and solving complex problems, but there are also concerns around it. "A lot of the algorithms, a lot of the data is racist," U.S. activist Tometi, who co-founded BLM in 2013, told Reuters on the sidelines of Lisbon's Web Summit. "We need tech to truly understand every way it (racism) shows up in the technologies they are developing," she said. The tech industry has faced a reckoning over the past few years over the ethics of AI technologies, with critics saying such systems could compromise privacy, target marginalised groups and normalise intrusive surveillance.