This feels like an extension of ethics, in general, not being part of the curriculum in education.
Anacondaâ€™s survey of data scientists from more than 100 countries foundÂ the ethics gap extends from academia to industry. While organizations can mitigate the problem through fairness tools and explainability solutions, neither appears to be gaining mass adoption.
Only 15% of respondents said their organization has implemented a fairness system, and just 19% reported they have an explainability tool in place.
The study authors warned that this could have far-reaching consequences:
Above and beyond the ethical concerns at play, a failure to proactively address these areas poses strategic risk to enterprises and institutions across competitive, financial, and even legal dimensions.
The survey also revealed concerns around the security of open-source tools and business training, and data drudgery.Â But itâ€™s the disregard of ethics that most troubled the study authors:
Of all the trends identified in our study, we find the slow progress to address bias and fairness, and to make machine learning explainable the most concerning. While these two issues are distinct, they are interrelated, and both pose important questions for society, industry, and academia.
While businesses and academics are increasingly talking about AI ethics, their words mean little if they donâ€™t turn into actions.
Teaching algorithms to create novel algorithms…
Artificial intelligence (AI) is evolvingâ€”literally. Researchers have created software that borrows concepts from Darwinian evolution, including â€œsurvival of the fittest,â€ to build AI programs that improve generation after generation without human input. The program replicated decades of AI research in a matter of days, and its designers think that one day, it could discover new approaches to AI.
â€œWhile most people were taking baby steps, they took a giant leap into the unknown,â€ says Risto Miikkulainen, a computer scientist at the University of Texas, Austin, who was not involved with the work. â€œThis is one of those papers that could launch a lot of future research.â€
Building an AI algorithm takes time. Take neural networks, a common type of machine learning used for translating languages and driving cars. These networks loosely mimic the structure of the brain and learn from training data by altering the strength of connections between artificial neurons. Smaller subcircuits of neurons carry out specific tasksâ€”for instance spotting road signsâ€”and researchers can spend months working out how to connect them so they work together seamlessly.
In recent years, scientists have sped up the process byÂ automating some steps. But these programs still rely on stitching together ready-made circuits designed by humans. That means the output is still limited by engineersâ€™ imaginations and their existing biases.
So Quoc Le, a computer scientist at Google, and colleagues developed a program called AutoML-Zero that could develop AI programs with effectively zero human input, using only basic mathematical concepts a high school student would know. â€œOur ultimate goal is to actually develop novel machine learning concepts that even researchers could not find,â€ he says.
An interesting article on business challenges with artificial intelligence.
Artificial intelligence (AI) technology continues to advance by leaps and bounds and is quickly becoming a potential disrupter and essential enabler for nearly every company in every industry. At this stage, one of the barriers to widespread AI deployment is no longer the technology itself; rather, itâ€™s a set of challenges that ironically are far more human: ethics, governance, and human values.
As AI expands into almost every aspect of modern life, the risks of misbehaving AI increase exponentiallyâ€”to a point where those risks can literally become a matter of life and death. Real-world examples of AI gone awry include systems that discriminate against people based on their race, age, or gender and social media systems that inadvertently spread rumors and disinformation and more.
Even worse, these examples are just the tip of the iceberg. As AI is deployed on a larger scale, the associated risks will likely only increaseâ€”potentially having serious consequences for society at large, and even greater consequences for the companies responsible. From a business perspective, these potential consequences include everything from lawsuits, regulatory fines, and angry customers to embarrassment, reputation damage, and destruction of shareholder value.
Yet with AI now becoming a required business capabilityâ€”not just a â€œnice to haveâ€â€”companies no longer have the option to avoid AIâ€™s unique risks simply by avoiding AI altogether. Instead, they must learn how to identify and manage AI risks effectively. In order to achieve the potential of human and machine collaboration, organizations need to communicate a plan for AI that is adopted and spoken from the mailroom to the boardroom. By having an ethical framework in place, organizations create a common language by which to articulate trust and help ensure integrity of data among all of their internal and external stakeholders. Having a common framework and lens to apply the governance and management of risks associated with AI consistently across the enterprise can enable faster, and more consistent adoption of AI.