audit


Enhancing trust in artificial intelligence: Audits and explanations can help 7wData

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There is a lively debate all over the world regarding AI's perceived "black box" problem. Most profoundly, if a machine can be taught to learn itself, how does it explain its conclusions? This issue comes up most frequently in the context of how to address possible algorithmic bias. One way to address this issue is to mandate a right to a human decision per the General Data Protection Regulation's (GDPR) Article 22. Here in the United States, Senators Wyden and Booker propose in the Algorithmic Accountability Act that companies be compelled to conduct impact assessments.


Mitigating Liability with XAI: The Case for Standardization Stanford Law School

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The legal value of XAI can be significant, especially (though by no means exclusively) in mitigating developer and end-user liability.¹ Though it is somewhat early to presume that the perfect information model introduced in The Role of Explainable AI (XAI) in Regulating AI Behavior: Delivery of "Perfect Information"can be viewed as a mature standard, the model does possess the necessary proto qualities and can be reasonably viewed as a proto standard. Therefore, a properly developed XAI is one that possesses, at a minimum, all the attributes of perfect information. And once that parameter is fixed, the XAI is deemed properly developed and ready to provide a variety of risk mitigation benefits. One example of how this can work is in dispositive-centric efforts, including in crafting safe harbors.


Hashbrown Out-of-Home Compliance, Audit & Monitoring

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The good news is that advertising spend displayed an unprecedented growth to reach expenditure of $8.3 billions. That was the US market. For 2019, OOH is expected to grow by 2.8 percent as new digital screens are likely to attract brands from sectors like technology, luxury, and travel. And Data sourced through WARC's global marketing index, digital and OOH were the only media to see marketing budgets grow around the world in March 2019. Other media channels registered declines.


Big Data and Artificial Intelligence are Set to Transform Accounting and Finance

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Spreadsheets were arguably the killer app for personal computers, and accounting software is commonly used. However, that hasn't changed day to day operations of businesses or the lives of individuals. We've simply shifted from written ledgers to digital ones, and accounting professionals review reports that haven't changed much beyond being computer generated. However, Big Data, AI and other technologies are poised to radically alter the financial world. Let's look at how Big Data and artificial intelligence are set to transform accounting and finance.


Are You Ready for AI-based Audit?

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A wide range of claims and news can be found online, from the technology "not being ready" to vendors declaring that AI is already within their tools. Ultimately, firms must decide when to invest in AI, or rather, which AI-enabled tool provides the best return on investment. By understanding how AI fits into their technology infrastructure and audit processes, audit managers and partners can most effectively choose the right solution. It's critical to understand what real AI and machine learning techniques mean, and the implications for the firm. So how do firms measure their readiness for AI and assess potential vendors?


Enhancing trust in artificial intelligence: Audits and explanations can help

#artificialintelligence

There is a lively debate all over the world regarding AI's perceived "black box" problem. Most profoundly, if a machine can be taught to learn itself, how does it explain its conclusions? This issue comes up most frequently in the context of how to address possible algorithmic bias. One way to address this issue is to mandate a right to a human decision per the General Data Protection Regulation's (GDPR) Article 22. Here in the United States, Senators Wyden and Booker propose in the Algorithmic Accountability Act that companies be compelled to conduct impact assessments.


High-Stakes AI Decisions Need to Be Automatically Audited

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Today's AI systems make weighty decisions regarding loans, medical diagnoses, parole, and more. They're also opaque systems, which makes them susceptible to bias. In the absence of transparency, we will never know why a 41-year-old white male and an 18-year-old black woman who commit similar crimes are assessed as "low risk" versus "high risk" by AI software. Oren Etzioni is CEO of the Allen Institute for Artificial Intelligence and a professor in the Allen School of Computer Science at the University of Washington. Tianhui Michael Li is founder and president of Pragmatic Data, a data science and AI training company.


Auditing with Artificial Intelligence

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Artificial intelligence has been with us for much longer than most people think. It's now over 20 years since the IBM supercomputer Deep Blue beat chess champion Garry Kasparov, a milestone moment when we began to wake up to the fact that a computer can match - and then outdo - what a human can. Since then, the conversation has largely been confined to the halls of academia and the secret labs of technology companies. It's only very recently that we have begun to see what AI can do in the real world. There are two questions that an internal audit function typically asks: Audit of AI - looking at all the uses of AI in the organisation, or Audit with AI - using AI to improve internal risk processes.


Is artificial intelligence (AI), machine learning (ML) suitable for small businesses?

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New Delhi: In order to make the business operations more efficient and automated, the deployment of software is required in place of manpower. It has been seen in all the businesses where computer machines have taken the place of a number of employees. With the incorporation of software which is set to work on designated protocols, an enterprise can become more cost-efficient and dynamic. Artificial intelligence (AI) and machine learning (ML) are some of the most widely used applications of Information Technology (IT) services which are thoroughly used in large enterprises. The usage of AI and machine learning software in small and medium enterprises (SMEs), small-scale businesses, seasonal businesses and conditional businesses to make the business process more robust is a big question today.


Opening Up the Black Box: Auditing Google's Top Stories Algorithm

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Auditing algorithms has emerged as a methodology for holding algorithms accountable by testing whether they are fair. This process often relies on the repeated use of a platform to record inputs and their corresponding outputs. For example, to audit Google search, one repeatedly inputs queries and captures the received search pages. The goal is then to discover, in the collected data, patterns that will reveal the ``secrets'' of algorithmic decision making. This knowledge discovery process makes some algorithm auditing tasks great applications for data mining techniques. In this paper, we introduce one particular algorithm audit, that of Google's Top stories. We describe the process of data collection, exploration, and analysis for this application and share some of the gleaned insights. Concretely, our analysis suggests that Google might be trying to burst the famous ``filter bubble'' by choosing less known publishers for the 3rd position in the Top stories.