What does it mean for AI to be “ethical”? As the use of artificial intelligence becomes more prominent across a slew of everyday processes and applications, questions about ethics in AI decision-making have risen to the forefront of public discussion.
With AI models playing a growing role in loan applications, investments, and many other financial services, the ethics of AI are an especially important topic for the fintech sector.
In the latest Fintech Alphabet episode, our panel of AI leaders joins PayU CEO Laurent le Moal to explore the challenges of building better ethics into AI – when human ethics can so often be their own black box. Whose ethics are we talking about in AI, and how does this impact the experience of others? When it comes to training an algorithm to quickly decide an ‘objective’ outcome under different scenarios, how can we quantify and measure what is an ‘ethical’ decision to make?
Watch the full episode below for an interesting discussion on AI ethics, alongside insights on starting up and scaling the use of AI tools, as well as other topics of conversation:
Who’s on the panel?
Jane Zavalishina, Co-Founder Mechanica AI. Mechanica AI uses machine learning and artificial intelligence to improve efficiency of industrial operations. Their AI-powered products deliver direct business outcomes through automated decision-making and process control. The proprietary technology is tailored to work with imperfect manufacturing data, allowing to bring the benefits of Industry 4.0 even to legacy assets. By combining the most advanced machine learning with domain knowledge, Mechanica ensures that their robust and reliable applications work seamlessly in the real-world production environment. Integrated as an additional intelligent layer, they help industrial companies unlock the value of digital transformation as well as operate in a more sustainable way. Founded in 2018, Mechanica AI serves customers in process industries, including metals and mining, oil and gas, chemicals, and others.
Hemant Misra, Vice President and Head of Applied Research, Swiggy. Founded in 2014, Swiggy is India’s leading, tech-driven, an on-demand delivery platform with a vision to elevate the quality of life for the urban consumer by offering unparalleled convenience. The platform is engineered to connect millions of consumers with hundreds of thousands of restaurants and stores across 500+ cities. Propelled by ML technology and fueled by terabytes of data processed every day, Swiggy offers a hassle-free, fast, and reliable delivery experience for millions of consumers, across the country. In addition, Swiggy’s New Supply is paving the path for new and innovative ways to solve the emerging demands of consumers. For every order delivered by Swiggy’s independent fleet of Delivery Partners, Swiggy uses Machine Learning capabilities across multiple domains (store/item ranking, meal prep time, travel time predictions, etc.), to ensure a host of customer-centric features – like lightning fast delivery, live order tracking – are effectively enabled.
Claire Gubian, Vice President & Global Head of Business Transformation, Dataiku. Dataiku is the centralized data platform that moves businesses along their data journey from analytics at scale to enterprise AI. By providing a common ground for data experts and explorers, a repository of best practices, shortcuts to machine learning and AI deployment/management, and a centralized, controlled environment, Dataiku is the catalyst for data-powered companies. Customers like Unilever, GE, and Comcast use Dataiku to ensure they are moving quickly and growing exponentially along with the amount of data they’re collecting. By removing roadblocks, Dataiku ensures more opportunity for business-impacting models and creative solutions, allowing teams to work faster and smarter.
Browse all episodes from our Fintech Alphabet series