Learning to trust artificial intelligence systems
Publié le 6 juillet 2018
For more than 100 years, we at IBM have been in the business of building machines designed to help improve the effectiveness and efficiency of people. And we’ve made measurable improvements to many of the systems that facilitate life on this planet. But we’ve never known a technology that can have a greater benefit to all of society than artificial intelligence.
At IBM, we are guided by the term “augmented intelligence” rather than “artificial intelligence.” This vision of “AI” is the critical difference between systems that enhance, improve and scale human expertise, and those that attempt to replicate human intelligence.
The ability of AI systems to transform vast amounts of complex, ambiguous information into insight has the potential to reveal long-held secrets and help solve some of the world’s most enduring problems. AI systems can potentially be used to help discover insights to treat disease, predict the weather, and manage the global economy. It is an undeniably powerful tool. And like all powerful tools, great care must be taken in its development and deployment.
To reap the societal benefits of AI systems, we will first need to trust it. The right level of trust will be earned through repeated experience, in the same way we learn to trust that an ATM will register a deposit, or that an automobile will stop when the brake is applied. Put simply, we trust things that behave as we expect them to.
But trust will also require a system of best practices that can help guide the safe and ethical management of AI systems including alignment with social norms and values; algorithmic responsibility; compliance with existing legislation and policy; assurance of the integrity of the data, algorithms and systems; and protection of privacy and personal information.
We consider this paper to be part of the global conversation on the need for safe, ethical and socially beneficial management of AI systems. To facilitate this dialogue, we are in the process of building an active community of thoughtful, informed thinkers that can evolve the ideas herein. Because there is
too much to gain from AI systems to let myth and misunderstanding steer us off our course. And while we don’t have all the answers yet, we’re confident that together we can address the concerns of the few to the benefit of many.
CATÉGORIESTechniques algorithmiques Types de systèmes algorithmiques Apprentissage (machine learning): supervisé (prédictif) ou non-supervisé Questions sociales, éthiques et juridiques Bulle filtrante Discrimination préjudiciable/illicite, discrimination souhaitée/légale Domaines d'application Ethique Explicabilité des algorithmes Evaluation expérimentales (peut se trouver aussi dans “Biais”) Gouvernance et auditabilité des algorithmes Opacité, asymétrie informationnelle Responsabilité, redevabilité