Only 35% of global consumers trust how AI is being implemented by organisations. AI outcomes can be biased or discriminatory and do not take into consideration the plurality and diversity of societies. Responsible AI is becoming the new business imperative. A few months ago, a leaked document from Facebook, now Meta, shook up the data privacy world. In the leaked document obtained by Motherboard, a group of privacy engineers working at Meta wrote, "We do not have an adequate level of control and explainability over how our systems use data, and thus we can't confidently make controlled policy changes or external commitments such as'we will not use X data for Y purpose.' And yet, this is exactly what regulators expect us to do, increasing our risk of mistakes and misrepresentation."

Various tools and practices have been developed to support practitioners in identifying, assessing, and mitigating fairness-related harms caused by AI systems. However, prior research has highlighted gaps between the intended design of these tools and practices and their use within particular contexts, including gaps caused by the role that organizational factors play in shaping fairness work. In this paper, we investigate these gaps for one such practice: disaggregated evaluations of AI systems, intended to uncover performance disparities between demographic groups. By conducting semi-structured interviews and structured workshops with thirty-three AI practitioners from ten teams at three technology companies, we identify practitioners' processes, challenges, and needs for support when designing disaggregated evaluations. We find that practitioners face challenges when choosing performance metrics, identifying the most relevant direct stakeholders and demographic groups on which to focus, and collecting datasets with which to conduct disaggregated evaluations. More generally, we identify impacts on fairness work stemming from a lack of engagement with direct stakeholders, business imperatives that prioritize customers over marginalized groups, and the drive to deploy AI systems at scale.

Improvements in cloud technologies and processing power have provided a solid foundation for mainstream adoption of machine learning (ML). With the ability to analyze massive amounts of data to derive meaningful insights, ML can give business leaders new ways to innovate, create new revenue streams, improve operational efficiencies, and help all employees make faster, more informed decisions. In IDG's 2019 Digital Business Study, 78% of IT and business leaders said their organizations are considering or have already deployed machine learning technologies as part of their digital business strategy. "We've seen it day in and day out with customers we support, and organizations in general, that are benefiting by leveraging machine learning," says Sri Elaprolu, senior leader, Amazon Machine Learning Solutions Lab, a team of data scientists and machine learning experts that helps Amazon Web Services (AWS) customers successfully adopt ML. Amazon is a prime example of how ML can impact every area of the business.

IBM's journey toward cognitive computing is well documented in the media. Cognitive computing is loosely defined as a simulating of human thought processes in a computerized model. It's a self-learning system that mimics the way the human brain works. Cognitive computing is an evolution of the artificial intelligence work that started in the 1960's. Many people are familiar with IBM's cognitive computing product, called Watson, through the famous Jeopardy episode where Watson won the game in 2011.

Organizations today recognize the critical importance of diversity. They address it by changing internal practices and establishing chief diversity officers to enable equal opportunities and to strive for greater inclusion so that teams with a wealth of cultures, beliefs, experiences and skills can make their companies even stronger. The realization that diverse teams achieve better outcomes than homogenous ones was further reinforced by a McKinsey study that found that the most ethnically and racially diverse companies had a better chance of outperforming their peers. Those companies had a 33% great probability of achieving above-average returns. Whether it's pricing stocks or determining guilt or innocence in a trial, a diverse group is more likely to examine the facts and be objective and accurate.

Conceptually, AI ethics applies to both the goal of the AI solution, as well as each part of the AI solution. AI can be used to achieve an unethical business outcome, even though its parts--machine learning, deep learning, NLP, and/or computer vision--were all designed to operate ethically. For example, an automated mortgage loan application system might include computer vision and tools designed to read hand-written loan applications, analyze the information provided by the applicant, and make an underwriting decision based on parameters programmed into the solution. These technologies don't process such data through an ethical lens--they just process data. Yet if the mortgage company inadvertently programs the system with goals or parameters that discriminate unfairly based on race, gender, or certain geographic information, the system could be used to make discriminatory loan approvals or denials.

In a sweeping bid to restore his country's image and elevate its standing in the global business community, French president Emmanuel Macron has hosted a string of international summits since taking office on issues such as tech and entrepreneurship to lure foreign investors with an overriding message: France is back. But to fuel the country's economic upswing, Macron's administration is also looking inward. Last month, it laid out a comprehensive blueprint authored by Fields Medal recipient Cedric Villani called "For a Meaningful Artificial Intelligence," which outlined a national strategy to become a leader in the field, overhaul digital innovation around AI and make sure labor-market and technological disruptions support inclusivity and diversity. "AI is not just a sector; it will shape an unprecedented transformation of society and the economy, and we're very proud to be ushering in a new AI era for France" Mounir Mahjoubi, France's secretary of state for digital affairs, told Adweek. "France is regarded as a leader in AI but needs to mobilize substantially more resources to bring in scientists, tech firms and dedicated research centers from the largest corporations," he said.

Vian Chinner is the founder of SA startup Xineoh, which specializes in the application of mathematical modeling and machine learning to ad technology. The "machine" in question here isn't quite the kind we might imagine though, but rather a clever algorithm used to monitor consumer's behavioral patterns, mining important data so as to anticipate purchasing decisions and offer relevant recommendations likely to have shoppers reaching deeper into their wallets. But while for years' machine learning has been the territory of a limited few, it's now fast becoming an imperative for businesses looking to understand and maximize the impact of their carefully monitored marketing spend. Where once we might have been amazed by Amazon.com's As AI starts to filter into our day-to-day lives, influencing everything from the music we listen to, to the coffees we order, it's becoming difficult for more traditional businesses to compete with their artificially assisted counterparts, who are better able to capitalize on shrinking attention spans and widespread time poverty.

What Is the Business Imperative for Cognitive Computing? Cognitive Computing is becoming the foundation for a new generation of solutions that have the potential to transform business. Unlike traditional approaches to building solutions, a cognitive computing approach allows the data to help determine the way applications are designed. This contrasts with conventional software development that begins with defining logic based on the current way a business operates. In her session at 18th Cloud Expo, Judith S. Hurwitz, President and CEO of Hurwitz & Associates, Inc., put cognitive computing into perspective with its value to the business.

The spring event season is coming to a close. And as usual, I'm feeling the rush of elation that comes from learning about exciting new technology, discovering smart new start-ups, and hearing industry experts talk about concepts like IoT, cognitive analytics, machine learning, neural networks and more. So many of us got into the IT industry because of technology's potential to change the world as we know it, so seeing first-hand the kind of technology capable of bringing that promise to fruition is great. But this year, something else accompanied the elation -- something I don't normally feel coming off the high of the event season. That's because when I talk with new and prospective customers, most of the conversations still revolve around the challenges of yesterday, to say nothing of today and tomorrow.