Al can be used to process and make decisions on data collected through IoT devices, so the two technologies are highly complementary. As the scale and complexity of IoT systems continues to grow, emerging technologies, such as machine learning and Al, are likely to lead to exponential growth in the volumes of data processed.
Any emergent technology presents new risks. Algorithmic bias, for example, is seen in decision-support systems that use machine learning and Al. With automation technology rather than humans making decisions, there is a risk of discrimination based on gender, race or sexual orientation.92
One example is an algorithm that was discarded by a major multinational business after it become clear it was unintentionally biased against women when applied to the hiring process.93 The algorithm was learning from previous applications to identify the optimal traits of potential candidates and, since most of the previous successful applicants were men, it was automatically favouring men over women in its decisions.
To protect all Australians, it is critical legislative frameworks can accommodate risks presented by new and emerging technologies. While this should include the review of the Privacy Act 1988 (Cth) announced in 2020, the need for ethical use of Al may be better met using voluntary industry codes of ethics and improved standards for how to avoid bias in automated decision-making.