The use of automated decision tools in recruitment has received an increasing amount of attention. In November 2021, the New York City Council passed a legislation (Local Law 144) that mandates bias audits of Automated Employment Decision Tools. From 15th April 2023, companies that use automated tools for hiring or promoting employees are required to have these systems audited by an independent entity. Auditors are asked to compute bias metrics that compare outcomes for different groups, based on sex/gender and race/ethnicity categories at a minimum. Local Law 144 proposes novel bias metrics for regression tasks (scenarios where the automated system scores candidates with a continuous range of values). A previous version of the legislation proposed a bias metric that compared the mean scores of different groups. The new revised bias metric compares the proportion of candidates in each group that falls above the median. In this paper, we argue that both metrics fail to capture distributional differences over the whole domain, and therefore cannot reliably detect bias. We first introduce two metrics, as possible alternatives to the legislation metrics. We then compare these metrics over a range of theoretical examples, for which the legislation proposed metrics seem to underestimate bias. Finally, we study real data and show that the legislation metrics can similarly fail in a real-world recruitment application.

A tweet pinned to the top of Hegde's feed in honor of Modi's birthday calls him "the leader who brought back India's lost glory." On January 7, the account tweeted a screenshot from ChatGPT to its more than 185,000 followers; the tweet appeared to show the AI-powered chatbot making a joke about the Hindu deity Krishna. ChatGPT uses large language models to provide detailed answers to text prompts, responding to questions about everything from legal problems to song lyrics. But on questions of faith, it's mostly trained to be circumspect, responding "I'm sorry, but I'm not programmed to make jokes about any religion or deity," when prompted to quip about Jesus Christ or Mohammed. That limitation appears not to include Hindu religious figures.

To conduct regulatory monitoring within Consumer and High Net Worth on a specific set of regulatory requirements (e.g., PEPS, Sanctions, EDD, FIC Amendment Bill, CTR, Waterfall (KYC), AML Training, Quality Assurance, etc.) as prescribed by the Regulatory Monitoring framework and drives first level of defence remediation of breaches. To provide insights on the state of regulatory adherence within allocated portfolio and prepare appropriate reports as input into overall regulatory reporting.

In recent years fairness in machine learning (ML), artificial intelligence (AI), and algorithmic decision-making systems has emerged as a highly active area of research and development. To date, the majority of measures and methods to mitigate bias and improve fairness in algorithmic systems have been built in isolation from policy and civil societal contexts and lack serious engagement with philosophical, political, legal, and economic theories of equality and distributive justice. Most define fairness in simple terms, where fairness means reducing gaps in performance or outcomes between demographic groups while preserving as much of the accuracy of the original system as possible. This oversimplification of equality through fairness measures is troubling. Many current fairness measures suffer from both fairness and performance degradation, or "levelling down," where fairness is achieved by making every group worse off, or by bringing better performing groups down to the level of the worst off.

How realistic is a future of AI hacking? If you buy something using links in our stories, we may earn a commission. This helps support our journalism. Its feasibility depends on the specific system being modeled and hacked. For an AI to even begin optimizing a solution, let alone develop a completely novel one, all of the rules of the environment must be formalized in a way the computer can understand.

SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today SpaceX is actively developing the technologies to make this possible, with the ultimate goal of enabling human life on Mars. At SpaceX we're leveraging our experience in building rockets and spacecraft to deploy Starlink, the world's most advanced broadband internet system. Starlink is the world's largest satellite constellation and is providing fast, reliable internet to 1M users worldwide. We design, build, test, and operate all parts of the system – thousands of satellites, consumer receivers that allow users to connect within minutes of unboxing, and the software that brings it all together.

For months, Getty Images has mumbled its simmering resentment over its photos being used for AI image generators. Now, the stock image site has finally turned up the heat on one of the companies crafting these AI systems. "Stable Diffusion at times produces images that are highly similar to and derivative of the Getty Images proprietary content that Stability AI copied extensively in the course of training the model," the lawsuit reads. Gizmodo reached out to Stability AI for comment, but we did not immediately hear back. Getty Images has already started similar legal proceedings in UK court.

Automatic document summarization aims to produce a concise summary covering the input document's salient information. Within a report document, the salient information can be scattered in the textual and non-textual content. However, existing document summarization datasets and methods usually focus on the text and filter out the non-textual content. Missing tabular data can limit produced summaries' informativeness, especially when summaries require covering quantitative descriptions of critical metrics in tables. Existing datasets and methods cannot meet the requirements of summarizing long text and multiple tables in each report. To deal with the scarcity of available data, we propose FINDSum, the first large-scale dataset for long text and multi-table summarization. Built on 21,125 annual reports from 3,794 companies, it has two subsets for summarizing each company's results of operations and liquidity. To summarize the long text and dozens of tables in each report, we present three types of summarization methods. Besides, we propose a set of evaluation metrics to assess the usage of numerical information in produced summaries. Dataset analyses and experimental results indicate the importance of jointly considering input textual and tabular data when summarizing report documents.

Language is an important form of data in politics. Constituents express their stances and needs in text such as social media and survey responses. Politicians conduct campaigns through debates, statements of policy positions, and social media. Government staff needs to compile information from various documents to assist in decision-making. Textual data is also prevalent through the documents and debates in the legislation process, negotiations and treaties to resolve international conflicts, and media such as news reports, social media, party platforms, and manifestos. Natural language processing (NLP) is the study of computational methods to automatically analyze text and extract meaningful information for subsequent analysis. The importance of NLP for policymaking has been highlighted since the last century (Gigley, 1993).

The right to be forgotten (RTBF) is motivated by the desire of people not to be perpetually disadvantaged by their past deeds. For this, data deletion needs to be deep and permanent, and should be removed from machine learning models. Researchers have proposed machine unlearning algorithms which aim to erase specific data from trained models more efficiently. However, these methods modify how data is fed into the model and how training is done, which may subsequently compromise AI ethics from the fairness perspective. To help software engineers make responsible decisions when adopting these unlearning methods, we present the first study on machine unlearning methods to reveal their fairness implications. We designed and conducted experiments on two typical machine unlearning methods (SISA and AmnesiacML) along with a retraining method (ORTR) as baseline using three fairness datasets under three different deletion strategies. Experimental results show that under non-uniform data deletion, SISA leads to better fairness compared with ORTR and AmnesiacML, while initial training and uniform data deletion do not necessarily affect the fairness of all three methods. These findings have exposed an important research problem in software engineering, and can help practitioners better understand the potential trade-offs on fairness when considering solutions for RTBF.