As AI becomes more interconnected with our daily lives, the ethical questions for companies and individuals have become more complex. Businesses realize the importance of ethical AI and the reputational damage that can stem from being associated with a prejudiced algorithm or one that produces unethical outputs, and this is driving change. A decade ago, AI ethics was perhaps an afterthought, regarded only in the most apparent cases of harmful output. Today, ethics are increasingly considered early in the AI project lifecycle and incorporated during the requirements gathering process.
Bias: a perennial challenge in AI
A few key ethical issues have been present since the early days of AI and continue to be important in a business context as technology evolves. The first is bias.
To fully understand the problem of bias, let’s start at the beginning of the lifecycle of an algorithm – a set of instructions and logical rules that execute to achieve an outcome, essentially the building blocks of AI. One of the first stages of creating an algorithm is gathering data on which to train the model with the challenge of making it robust. In many cases, priority goes to the quantity of training data over its quality or representativeness (in terms of both the content itself being representative and coming from a diverse and representative set of sources). An algorithm may be given diverse content from the internet or other public sources as training data, and the quality of web content cannot always be ensured. Within a set of data scraped from the web, certain populations might be over- or under-represented, bias in how content is presented, and the content itself may even be false. If an algorithm is trained on biased data, its output is likely biased, and the impact can be far-reaching.
The risk of malicious manipulation of algorithms
Another issue in AI ethics that could become more prominent as technology evolves is the malicious use of algorithms. This issue is perhaps more straightforward and less prevalent than the issue of bias, making it a less significant threat in a business context.
Continue reading: https://www.spiceworks.com/tech/artificial-intelligence/guest-article/forging-a-future-with-ethical-ai/
Bias: a perennial challenge in AI
A few key ethical issues have been present since the early days of AI and continue to be important in a business context as technology evolves. The first is bias.
To fully understand the problem of bias, let’s start at the beginning of the lifecycle of an algorithm – a set of instructions and logical rules that execute to achieve an outcome, essentially the building blocks of AI. One of the first stages of creating an algorithm is gathering data on which to train the model with the challenge of making it robust. In many cases, priority goes to the quantity of training data over its quality or representativeness (in terms of both the content itself being representative and coming from a diverse and representative set of sources). An algorithm may be given diverse content from the internet or other public sources as training data, and the quality of web content cannot always be ensured. Within a set of data scraped from the web, certain populations might be over- or under-represented, bias in how content is presented, and the content itself may even be false. If an algorithm is trained on biased data, its output is likely biased, and the impact can be far-reaching.
The risk of malicious manipulation of algorithms
Another issue in AI ethics that could become more prominent as technology evolves is the malicious use of algorithms. This issue is perhaps more straightforward and less prevalent than the issue of bias, making it a less significant threat in a business context.
Continue reading: https://www.spiceworks.com/tech/artificial-intelligence/guest-article/forging-a-future-with-ethical-ai/
