Commercial contracts are known to be a valuable source for deriving project-specific requirements. However, contract negotiations mainly occur among the legal counsel of the parties involved. The participation of non-legal stakeholders, including requirement analysts, engineers, and solution architects, whose primary responsibility lies in ensuring the seamless implementation of contractual terms, is often indirect and inadequate. Consequently, a significant number of sentences in contractual clauses, though legally accurate, can appear unfair from an implementation perspective to non-legal stakeholders. This perception poses a problem since requirements indicated in the clauses are obligatory and can involve punitive measures and penalties if not implemented as committed in the contract. Therefore, the identification of potentially unfair clauses in contracts becomes crucial. In this work, we conduct an empirical study to analyze the perspectives of different stakeholders regarding contractual fairness. We then investigate the ability of Pre-trained Language Models (PLMs) to identify unfairness in contractual sentences by comparing chain of thought prompting and semi-supervised fine-tuning approaches. Using BERT-based fine-tuning, we achieved an accuracy of 84% on a dataset consisting of proprietary contracts. It outperformed chain of thought prompting using Vicuna-13B by a margin of 9%.

Artificial intelligence is being used across the healthcare industry with the goal of delivering care more efficiently and improving outcomes for patients. But if health systems and vendors aren't careful, AI has the potential to support biased decision-making and make equities even worse. "Algorithmic bias really is the application of an algorithm that compounds existing inequity," Sarah Awan, equity fellow with CEO Action for Racial Equity and senior manager at PwC, said in a seminar hosted by the Digital Medicine Society and the Consumer Technology Association. "And that might be in socioeconomic status, race and ethnic background, religion, gender, disability, sexual orientation, etc. So while AI can help identify bias and reduce human bias, it really also has the power for bias at scale in very sensitive applications." Healthcare is behind other industries when it comes to using data analytics, said Milissa Campbell, managing director and health insights lead at NTT DATA Services.

Companies using artificial intelligence (AI) across their business units should consider creating a C-suite position to oversee how AI is used and guard against the risk of making bad decisions based on biased algorithms, experts say. Only a few companies, like Levi Strauss & Co, have established a chief artificial intelligence officer (CAIO) position, and fewer have created a C-level position dedicated solely to AI ethics. Brian Kropp, chief of research in the HR practice at Gartner, said chief technology officers and chief information officers will struggle with handling AI-related decisions and ethical dilemmas. "CTOs and CIOs are going to be thinking about the role through the lens of how they can make the technology work," Kropp said. However, "artificial intelligence is not a question of how you get the technology to work; it's a question of how do you think through the implications of the technology?"