The U.S. Equal Employment Opportunity Commission will examine how artificial intelligence tools are "fundamentally changing" employment decisions. The workplace civil rights agency will establish a new internal working group to study how employers use AI for hiring, promotions, and firing workers, according to a Thursday statement. It also will host "listening sessions" with stakeholders and aims to issue technical assistance "to provide guidance on algorithmic fairness." "The EEOC is keenly aware that these tools may mask and perpetuate bias or create new discriminatory barriers to jobs," said EEOC Chair Charlotte Burrows in a statement. "We must work to ensure that ...

In various ways, AI-based SEO marketing helps in improving digital marketing strategies. In this era of online marketing, artificial intelligence has played a big role. Algorithms of search engine optimization have changed drastically in the past few years due to which old SEO strategies are not working on Google. It means now it is necessary to understand the role of AI when optimizing for search to rank pages. First, let's understand the meaning of Artificial Intelligence.

The U.S. Centers for Disease Control and Prevention estimates that one in seven children in the United States experienced abuse or neglect in the past year. Child protective services agencies around the nation receive a high number of reports each year (about 4.4 million in 2019) of alleged neglect or abuse. With so many cases, some agencies are implementing machine learning models to help child welfare specialists screen cases and determine which to recommend for further investigation. But these models don't do any good if the humans they are intended to help don't understand or trust their outputs. Researchers at MIT and elsewhere launched a research project to identify and tackle machine learning usability challenges in child welfare screening.

Given Spatial Variability Aware Neural Networks (SVANNs), the goal is to investigate mathematical (or computational) models for comparative physical interpretation towards their transparency (e.g., simulatibility, decomposability and algorithmic transparency). This problem is important due to important use-cases such as reusability, debugging, and explainability to a jury in a court of law. Challenges include a large number of model parameters, vacuous bounds on generalization performance of neural networks, risk of overfitting, sensitivity to noise, etc., which all detract from the ability to interpret the models. Related work on either model-specific or model-agnostic post-hoc interpretation is limited due to a lack of consideration of physical constraints (e.g., mass balance) and properties (e.g., second law of geography). This work investigates physical interpretation of SVANNs using novel comparative approaches based on geographically heterogeneous features. The proposed approach on feature-based physical interpretation is evaluated using a case-study on wetland mapping. The proposed physical interpretation improves the transparency of SVANN models and the analytical results highlight the trade-off between model transparency and model performance (e.g., F1-score). We also describe an interpretation based on geographically heterogeneous processes modeled as partial differential equations (PDEs).

How to develop an ethical and non-biased AI application in an undoubtedly biased and unbalanced society? Can AI be the holy grail by developing more balanced societies that overcome traditional inequality and exclusion? It is too early to say, and it seems apparent that we will witness many trial-and-error phases before achieving a consensus on what and how AI might be used ethically in our societies. Much like institutional racism, which requires fundamental shifts in the overall ecosystem, the problems in AI development also call for a similar change to create better output. Behind the development and implementation of algorithms, there are developers and specific people in power positions.

As the range of potential uses for Artificial Intelligence (AI), in particular machine learning (ML), has increased, so has awareness of the associated ethical issues. This increased awareness has led to the realisation that existing legislation and regulation provides insufficient protection to individuals, groups, society, and the environment from AI harms. In response to this realisation, there has been a proliferation of principle-based ethics codes, guidelines and frameworks. However, it has become increasingly clear that a significant gap exists between the theory of AI ethics principles and the practical design of AI systems. In previous work, we analysed whether it is possible to close this gap between the'what' and the'how' of AI ethics through the use of tools and methods designed to help AI developers, engineers, and designers translate principles into practice.

Maura R. Grossman, JD, Ph.D., is a Research Professor in the Cheriton School of Computer Science, an Adjunct Professor at Osgoode Hall Law School, and an affiliate faculty member of the Vector Institute for Artificial Intelligence. She is also Principal at Maura Grossman Law, an eDiscovery law and consulting firm in Buffalo, New York. Maura is best known for her work on technology-assisted review, a supervised machine learning approach that she and her colleague, Computer Science Professor Gordon V. Cormack, developed to expedite review of documents in high-stakes litigation. She teaches Artificial Intelligence: Law, Ethics, and Policy, a course for graduate computer science students at Waterloo and upper-class law students at Osgoode, as well as the ethics workshop required of all students in the master's programs in artificial intelligence and data science at Waterloo. Artificial intelligence is an umbrella term first used at a conference in Dartmouth in 1956.

In April of 2021, the U.S. Federal Trade Commission -- in its "Aiming for truth, fairness, and equity in your company's use of AI" report -- issued a clear warning to tech industry players employing artificial intelligence: "Hold yourself accountable, or be ready for the FTC to do it for you." Likewise, the European Commission has proposed new AI rules to protect citizens from AI-based discrimination. These warnings, and impending regulations, are warranted. Machine learning (ML), a common type of AI, mimics patterns, attitudes and behaviors that exist in our imperfect world, and as a result, it often codifies inherent biases and systemic racism. Unconscious biases are particularly difficult to overcome, because they, by definition, exist without human awareness.

The U.S. Centers for Disease Control and Prevention estimates that one in seven children in the United States experienced abuse or neglect in the past year. Child protective services agencies around the nation receive a high number of reports each year (about 4.4 million in 2019) of alleged neglect or abuse. With so many cases, some agencies are implementing machine learning models to help child welfare specialists screen cases and determine which to recommend for further investigation. But these models don't do any good if the humans they are intended to help don't understand or trust their outputs. Researchers at MIT and elsewhere launched a research project to identify and tackle machine learning usability challenges in child welfare screening.

The U.S. Centers for Disease Control and Prevention estimates that one in seven children in the United States experienced abuse or neglect in the past year. Child protective services agencies around the nation receive a high number of reports each year (about 4.4 million in 2019) of alleged neglect or abuse. With so many cases, some agencies are implementing machine learning models to help child welfare specialists screen cases and determine which to recommend for further investigation. But these models don't do any good if the humans they are intended to help don't understand or trust their outputs. Researchers at MIT and elsewhere launched a research project to identify and tackle machine learning usability challenges in child welfare screening.