Can Artificial Intelligence Be Biased?

In pursuit of automation-driven efficiencies, the rapidly evolving artificial intelligence (AI) tools and techniques (such as neural networks, machine-learning, predictive analytics, speech recognition, natural-language processing and more) are now routinely used across nations: its governments, industries, organizations and academia (NGIOA) for navigation, translation, behavior modeling, robotic control, risk management, security, decision making and many other applications. As AI is becoming democratized, these evolving intelligent algorithms are now rapidly becoming prevalent in most, if not all, aspects of human and machine decision-making. While Decision Utilities like intelligent algorithms have been in use for many years, there are rising concerns about the general lack of algorithmic understanding, usage practices, the rapidly penetrating bias in automated decisions, and the lack of transparency and accountability. As a result, ensuring integrity, transparency and trust in algorithmic decision-making is becoming a complex challenge for the creators of algorithms with huge implications for the future of society. Irrespective of cyberspace, geospace or space (CGS), since technology revolutions are driven not just by accidental discovery but also by societal needs, the question we all individually and collectively need to first and foremost evaluate is whether there really is a need for decision-making algorithms--and if yes, where and why.