AI for good: Three principles to use AI at work responsibly
AI for good: Three Principles for Positive Impact
The impact of AI at work has been inspirational. We’ve been inspired by stories of artificial intelligence (AI) working in tandem with people and automation to achieve greater business potential—accelerating the creative process, augmenting human abilities, and enhancing productivity. But we can’t ignore the capacity of AI to be used for harm as well.
The increasing adoption of Generative AI models has sparked concerns about their safety, security, and, notably, data privacy. A study conducted by MIT Sloan Management Review and Boston Consulting Group revealed that 84% of AI experts and implementers consider responsible AI a crucial management concern. However, just over half (56%) believe it’s being taken seriously enough by business leaders.
Consider the damage that could be done by biased training data that warps AI decision making, or the capacity of these systems to spread misinformation on a massive scale. There’s a lack of transparency in how these systems work, and the risk that the data you share with them could be used for training and appear in front of another user.
1. Creating an open ecosystem for AI excellence:
We cannot believe a company will ever have a monopoly over the best AI capabilities. After all, ‘AI’ refers to a diverse range of different tools and technologies. There’s Generative AI, of course, but also Specialized AI which includes models that are trained for a specific business task or process, like document processing or sentiment analysis. Each has value for the enterprise and no company can ever be the best for all of them. We aim to maintain this diversity for the benefit of our customers and the AI industry.
2. Flexible AI that adapts to the user—not the other way around
It’s vital, as creators of AI solutions, that our models can adapt to users’ needs. When a customer has no choice but to choose an off-the-shelf model that doesn’t quite meet their use case, that’s when accidents happen. Accuracy means the world when you’re using AI to make decisions that directly impact your customers, or which need to be compliant.
3. Guardrails for responsible AI
AI systems need data to improve. But users deserve the right to know their data is protected. UiPath has a responsibility to ensure data used to build our tools is of good quality, sourced lawfully, and securely managed. We do this in a variety of ways:
(A) Legal and compliant data collection
(B) Appropriate measures so data processed in the UiPath Platform is protected
(C) Accurate and balanced data in UiPath models and algorithms to address bias in the training data
(D) GDPR compliance, including for data deletion
The platform is actively reviewed for privacy, bias, and data security concerns, aiding the development of trustworthy AI. We’ve even achieved some innovation here. Usually, inaccurate or biased models are the result of poor training rather than bad intentions. It can be difficult, especially for ordinary business users, to know when their AI model is sufficiently trained and balanced. UiPath Communications Mining solves this problem through its Model Rating feature. The capability largely automates the process of model validation, assigning it a score for performance, accuracy, and balance.
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