1. Concerns about bias and fairness in AI systems:
As relatively new technologies emerge, the challenges surrounding the development of, in this case, artificial intelligence (AI), become greater. A global research agenda has emerged to tackle what is arguably most important: AI systems can be biased. As these systems typically rely on large databases, they can account for geographic bias and have a negative impact or disparity. Contacts embedded in recruitment frameworks or specialist databases may be pigeonholed to some extent due to racial or gender bias. To counter this, there should be a clear emphasis on fairness, more emphasis on covering the diversity of data, particularly when building models, and greater engagement in experimentation while by developing the models and inducing corrective reactions to bias outbreaks.
2. Privacy and data management setbacks for smart technologies dependent on third parties:
Privacy is the third major area of concern. Most AI systems are constrained and must use a considerable number of personal attributes to be effective. This raises questions about how this data is obtained, stored and used. If such safeguards are not put in place, AI systems may also encounter privacy concerns regarding the illegal acquisition or use of sensitive data. Legal structures like those in Europe such as…
