In an era where artificial intelligence is no longer the realm of science fiction but an integral part of our daily lives, the ethical implications of AI deployments have moved from theoretical academic debates to pressing real-world concerns. As AI systems become increasingly integrated into every aspect of our phygital existence, the question is no longer what AI can do, but what it should responsibly do. I had the opportunity to interview Francesca Rossi, IBM Fellow and Global Leader for AI Ethics at IBM, to delve deeper into these complex questions.
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Francesca Rossi is no stranger to the ethical dilemmas posed by AI. With more than 220 scientific articles to her credit and leadership roles at organizations like AAAI and the AI Partnership, she is at the forefront of how we think about AI ethics today and building an AI we can all trust.
The ethical challenges of rapid growth in AI
“AI is growing rapidly – it is used in many of the services consumers interact with today. This is why it is so important to address the ethical challenges that AI can raise,” Rossi began. She highlighted the crucial need for users to trust AI systems, emphasizing that trust depends on explainability and transparency.
“For users, it is ethically important to be able to trust the recommendations of an AI system. To achieve this, AI requires explainability and transparency,” she said. But trust isn’t the only concern. Rossi emphasized that data processing, privacy and copyright protection are also important ethical challenges that must be addressed head on.
When asked how IBM defines “responsible AI,” Rossi detailed a comprehensive framework that goes beyond simple principles to include practical implementations.
“IBM has built a comprehensive AI ethics framework, which includes both the principles and their implementations, with the aim of guiding the design, development, deployment and use of AI within for IBM and for our customers,” she explained.
The principles are simple but profound, according to Rossi:
- The goal of AI is to augment human intelligence.
- Data and information belong to their creator.
- New technologies, including AI systems, must be transparent and explainable.
But principles alone are not enough. Rossi emphasized the importance of translating these principles into action: “Implementation of these principles includes risk assessment processes, education and training activities, software tools, development manuals for developers , an integrated governance program, research innovation and centralized enterprise-wide governance in the form of an AI Ethics Committee.
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IBM’s commitment to open and transparent innovation is also evident. “We have released our Granite family of models to the open source community under an Apache 2.0 license for broad, unconstrained commercial use, along with tools to monitor model data – ensuring it conforms to the standards required by responsible business applications. » Rossi added.
Collaboration with policy makers is essential
The role of policymakers in AI ethics is a hot topic, and Rossi believes collaboration between businesses and governments is crucial.
“As a trusted leader in AI, IBM sees the need for smart AI regulation that provides guardrails for AI uses while promoting innovation,” he said. -she declared. IBM is urging governments around the world to focus on risk-based regulation, prioritize accountability over licensing, and support open source AI innovation.
“While there are many individual companies, start-ups, researchers, governments and others committed to open science and technology, more collaboration and information sharing will help the community innovate more quickly and more inclusively, and to identify specific risks, in order to mitigate risks. before putting a product into the world,” emphasized Rossi.
One might wonder how these high-level principles translate into practical measures within IBM’s AI systems. Rossi provided concrete examples: “IBM developed user-friendly bias mitigation approaches, proposed methods to understand the differences between AI models in an interpretable way, studied maintenance of AI models from the perspective of robustness and created methods to understand the activation space of neural networks. for various trustworthy AI tasks.
She also mentioned that IBM has analyzed adversarial vulnerabilities in AI models and proposed training approaches to mitigate these vulnerabilities. “We have made significant updates to our AIexplainability 360 toolkit to support time series and industrial use cases, and have developed application-specific frameworks for trustworthy AI,” she said. added.
AI innovation within ethical boundaries
A common concern is whether strict ethical guidelines stifle innovation. On the contrary, Rossi sees ethics as an enabler rather than an obstacle. “AI can drive enormous progress for business and society, but only if it is trusted,” she said.
She cited IBM’s annual AI Adoption Index, noting that while 42% of companies enterprise-wide have deployed AI, 40% are still exploring or experimenting without deployment. “Ongoing challenges to AI adoption in businesses remain, including hiring employees with the right skills, data complexity and ethical concerns,” Rossi said. “Businesses must prioritize AI ethics and trustworthy AI to successfully deploy technology and encourage further innovation. »
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Building AI systems that prioritize ethical considerations is no easy feat. Rossi acknowledged the obstacles: “We see that a large percentage of companies are stuck in the experimentation and exploration phase, highlighting a dramatic gap between the hype around AI and its actual use. »
She highlighted that challenges such as the skills gap, data complexity, and AI trust and governance pose significant obstacles. “IBM’s annual Global AI Adoption Index recently found that while around 85% of businesses agree that trust is key to unlocking the potential of AI, far fewer than half are taking measures to achieve truly trustworthy AI, and only 27% of them focus on reducing bias,” she noted.
To address these challenges, IBM launched Watsonx, an enterprise-ready AI and data platform. “This accelerates the development of trustworthy AI and provides the visibility and governance needed to ensure AI is used responsibly,” Rossi explained.
India’s role in shaping global AI ethics
India is quickly emerging as a major player in AI innovation, and Rossi believes the country has an important role to play in shaping global AI ethics and governance.
“Given that the AI market in India is growing at a rapid pace, with some estimates suggesting it is growing at 25-35% and expected to reach $17 billion by 2027, ethics and AI governance will be critical as the market continues to grow,” she said.
She highlighted recent initiatives such as the Global IndiaAI Summit 2024 organized by the Ministry of Electronics and Information Technology (MeitY), which aimed to advance the development of AI in areas such as capacity computing, fundamental models, datasets, application development, future skills, startup funding and safe AI.
Given India’s growing talent pool in the fields of AI and data science, education and training in AI ethics is paramount. Rossi mentioned that IBM researchers in India are focusing on the ethical challenges of AI at all three of IBM’s labs in the country: IBM Research India, IBM India Software Labs and IBM Systems Development Labs.
“These labs are closely aligned with our strategy, and their pioneering work in AI, cloud, cybersecurity, sustainability and automation is integrated into IBM products, solutions and services,” she said.
The Future of AI Ethics
Looking ahead, Rossi is optimistic but cautious about how AI ethics will evolve over the next decade. “Investing in AI ethics is crucial for long-term profitability, as ethical AI practices improve brand reputation, build trust and ensure compliance with evolving regulations,” he said. -she affirmed.
IBM is actively building a robust ecosystem to advance ethical and open innovation around AI. “We recently collaborated with Meta and more than 120 other open source leaders to launch the AI Alliance, a group whose mission is to create and support open technology for AI and the open communities that will enable it to benefit all”, Rossi commune.
As AI becomes more interconnected and integrated into our lives, new ethical challenges will arise. Rossi emphasized the importance of focusing on trust in the era of powerful foundation models.
“In line with our focus on trustworthy AI, IBM is developing solutions for upcoming challenges such as robustness, uncertainty quantification, explainability, data drift, privacy and concept drift in models of AI,” she said.
The TLDR version of my interview with IBM’s Francesca Rossi consistently highlights a fundamental truth: ethical considerations in AI are not optional: they are essential to lasting success. As Rossi says so well: “These considerations are not opposed to profit but rather are essential to lasting success.”
As AI’s influence only grows, Francesca Rossi’s ideas offer a roadmap for navigating AI’s complex ethical landscape. It’s an effort that requires transparency, collaboration, and an unwavering commitment to building systems that not only advance technology, but also uphold the values that define us as a society. A collective effort that involves policymakers, educators and industry leaders here in India and around the world.
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Jayesh Shinde
Editor-in-chief at Digit. Technology journalist since January 2008, with stints at Indiatimes.com and PCWorld.in. Enthusiastic dad, reluctant traveler, weekend gamer, LOTR nerd, pseudo bon vivant. View full profile
