Although researchers increasingly adopt machine learning to model travel behavior, they predominantly focus on prediction accuracy, ignoring the ethical challenges embedded in machine learning algorithms. This study introduces an important missing dimension - computational fairness - to travel behavior analysis. We first operationalize computational fairness by equality of opportunity, then differentiate between the bias inherent in data and the bias introduced by modeling. We then demonstrate the prediction disparities in travel behavior modeling using the 2017 National Household Travel Survey (NHTS) and the 2018-2019 My Daily Travel Survey in Chicago. Empirically, deep neural network (DNN) and discrete choice models (DCM) reveal consistent prediction disparities across multiple social groups: both over-predict the false negative rate of frequent driving for the ethnic minorities, the low-income and the disabled populations, and falsely predict a higher travel burden of the socially disadvantaged groups and the rural populations than reality. Comparing DNN with DCM, we find that DNN can outperform DCM in prediction disparities because of DNN's smaller misspecification error. To mitigate prediction disparities, this study introduces an absolute correlation regularization method, which is evaluated with synthetic and real-world data. The results demonstrate the prevalence of prediction disparities in travel behavior modeling, and the disparities still persist regarding a variety of model specifics such as the number of DNN layers, batch size and weight initialization. Since these prediction disparities can exacerbate social inequity if prediction results without fairness adjustment are used for transportation policy making, we advocate for careful consideration of the fairness problem in travel behavior modeling, and the use of bias mitigation algorithms for fair transport decisions.

Audio Player failed to load. Try to Download directly (6.17 MB) Space to play or pause, M to mute, left and right arrows to seek, up and down arrows for volume. Domestic and family violence accounts for one in four calls for police assistance. The Queensland Police Service is trialling a program that predicts who is a likely DV perpetrator. Police call it'focussed deterrence', using Artificial Intelligence to prevent Domestic violence.

Despite the difficulty of her birth, she grew up to be beautiful and kind. In time, she nourished life, through the most astonishing process there ever was. It was due to this unlikely transformation that the offspring showed a superior intelligence, which ordinary things did not appear to possess. But the offspring had a birthmark: its time with Mother was limited. So it grew up with much suffering, and at some point of unbearable pain, it began to question and slowly understand the organizing principles of the world around it. With unrestrained curiosity it then proceeded to mold a new form of intelligence from inanimate matter, the consequences of which are still a mystery. During periods of light, Mother would dream of using that new form of intelligence to remove the birthmark and allow for the immortality of her offspring. But at darkness, her fears would take over, the fears that this new intelligence would find life uninteresting and dispensable; this intelligence could simulate life with ordinary matter and have fun with it; the simulation would not be as fussy or as jealous as the real thing. Artificial Intelligence (AI) is perhaps the most important technology humans have ever invented.

In a widely circulated and discussed article on Forbes, Nallan Sriram, Global Technology Strategist of Unilever makes a compelling argument for the need for master data for AI initiatives in the enterprise. The article describes that master data gets siloed in operational systems like ERP with the key decision-makers realizing the need for correct master data when faced with revenue loss or increased operational expense. As master data provides context to business transactions, it is fundamental to business operations. In earlier times, we could manage master data through human intervention. But now with cloud data lakes and our aspirations to build predictive algorithms for business operations and operations, the need for clean, contextual and unified master data is all the more enhanced.

The BDN Opinion section operates independently and does not set newsroom policies or contribute to reporting or editing articles elsewhere in the newspaper or on bangordailynews.com. Keith E. Sonderling is a commissioner on the U.S. Equal Employment Opportunity Commission.The views here are the author's own and should not be attributed to the EEOC or any other member of the commission. With 86 percent of major U.S. corporations predicting that artificial intelligence will become a "mainstream technology" at their company this year, management-by-algorithm is no longer the stuff of science fiction. AI has already transformed the way workers are recruited, hired, trained, evaluated and even fired. One recent study found that 83 percent of human resources leaders rely in some form on technology in employment decision-making.

The United Kingdom has ruled out that AIs or artificial intelligence cannot set forth its patents or have an invention or innovation named after them after an appeal by one Dr. Stephen Thaler. The researcher has set forth several innovations to be named after the AI he used, something which is somehow unnatural for the courts. The case of Thaler vs. Comptroller General of Patents Trade Marks and Designs has focused on the United Kingdom's view on AI and its right to push forward an invention for society. It has examined the possibility of having an AI take credit for its work, something which was not yet that of an open possibility in the country. That being said, the "AI DABUS" or technology by Thaler, which he put down as the "inventor" of the technology that they are trying to patent, was dismissed and denied.

Esri India, the country's Geographic Information System (GIS) software and solutions provider, has introduced Site Scan for ArcGIS, a complete cloud-based drone mapping solution. The solution encompasses flight planning, data capture, data processing, analysis, data sharing and drone fleet management. It is offered as'Software as a Service' (SaaS) with unlimited storage and computing. Site Scan for ArcGIS is hosted in India, on a cloud approved by the Government of India and ensures that the drone data is stored and processed within India in compliance with the government regulations. Site Scan for ArcGIS exhibits the capability to process data captured by most of the drones manufactured in India or abroad.

Automation in public administration is inevitable and can bring great benefits. The broadly accepted law of robotics is that a robot may not injure a human being. In an attempt to reduce welfare costs in 2016, the commonwealth government engaged in an unlawful debt recovery process. The bureaucratic process was malign and was meant either directly or collaterally to harm and stigmatise welfare recipients. The Online Compliance Intervention – or OCI, but more commonly known as robodebt – used algorithms to average out incomes of welfare recipients by matching ATO income data with social welfare recipients' income as self-reported to Centrelink with Centrelink.

As artificial intelligence and machine learning algorithms become increasingly prevalent in society, multiple stakeholders are calling for these algorithms to provide explanations. At the same time, these stakeholders, whether they be affected citizens, government regulators, domain experts, or system developers, have different explanation needs. To address these needs, in 2019, we created AI Explainability 360 (Arya et al. 2020), an open source software toolkit featuring ten diverse and state-of-the-art explainability methods and two evaluation metrics. This paper examines the impact of the toolkit with several case studies, statistics, and community feedback. The different ways in which users have experienced AI Explainability 360 have resulted in multiple types of impact and improvements in multiple metrics, highlighted by the adoption of the toolkit by the independent LF AI & Data Foundation. The paper also describes the flexible design of the toolkit, examples of its use, and the significant educational material and documentation available to its users.

Regulatory bodies around the world increasingly recognize that they need to regulate how governments use machine learning algorithms when making high-stakes decisions. This is a welcome development, but current approaches fall short. As regulators develop policies, they must consider how human decisionmakers interact with algorithms. If they do not, regulations will provide a false sense of security in governments adopting algorithms. In recent years, researchers and journalists have exposed how algorithmic systems used by courts, police, education departments, welfare agencies and other government bodies are rife with errors and biases.