Data ethics: It’s a thing

Published 29-MAR-2019 16:40 P.M.

|

3 minute read

Hey! Looks like you have stumbled on the section of our website where we have archived articles from our old business model.

In 2019 the original founding team returned to run Next Investors, we changed our business model to only write about stocks we carefully research and are invested in for the long term.

The below articles were written under our previous business model. We have kept these articles online here for your reference.

Our new mission is to build a high performing ASX micro cap investment portfolio and share our research, analysis and investment strategy with our readers.


Click Here to View Latest Articles

In light of the Ethics of Data Science conference that concluded yesterday, it’s worth looking at how ethical algorithms can actually be.

Think about it: everything from self-driving cars to automated personal assistants, artificial intelligence is well on its way to impacting every facet of our daily lives.

This means industries and governments are relying on machine learning to make important decisions that will have a real effect on the lives of consumers and citizens.

The conference is hosted by the University of Sydney, which tells us: “algorithms are a fundamental tool in everyday machine learning and artificial intelligence, but experts have identified a number of ethical problems. Models built with biased and inaccurate data can have serious implications and dangerous consequences, ranging from the legal and safety implications of self-driving cars and incorrect criminal sentencing, to the use of automated weapons in war.”

It’s unlikely the world will go all WarGames, but it is a possibility if the right data gets into the wrong hands.

So what can be done?

Director of the Centre for Translational Data Science, Professor Sally Cripps believes data experts must understand how to quantify uncertainty to prevent bias.

The Centre uses data science to preserve natural resources, build intelligent systems, improve digital health and explore the human condition.

“It is important to note that algorithms are not unethical, it is the bias in sampling created by some implementations of them which is an issue,” Professor Cripps explained.

Taking domestic violence as an example Professor Cripps said, “If an algorithm finds that a subgroup of the population is more likely to experience domestic violence, and on that basis continues to sample from that subgroup, then it is a self-fulfilling prophecy. To guard against this, a deep understanding of uncertainty and how to quantify it needs to be incorporated into algorithms.”

When it comes to business ethics, it is a matter of defining business ethics in a digital world. When you profit from data use, how do you achieve ethical practice?

Jason Tan, CEO and co-founder of machine learning company Sift Science told SecurityIntelligence, “Each business needs to define for itself a clear North Star of what is right and what is wrong. That doesn’t have to get into the nitty-gritty of what is right and wrong — but establish a baseline of what they want for a cultural mindset so that everyone is guided by the principle of doing the right thing as much as possible.”

That isn’t so easy because what is considered right or wrong is often a grey area.

Accenture says, The digital economy is built on data—massive streams of data being created, collected, combined and shared—for which traditional governance frameworks and risk-mitigation strategies are insufficient. In the digital age, analyzing and acting on insights from data can introduce entirely new classes of risk. These include unethical or even illegal use of insights, amplifying biases that exacerbate issues of social and economic justice, and using data for purposes to which its original disclosers would not have agreed, and without their consent. These and other practices can permanently damage consumer trust in a brand.

No company, wants that. And imagine if a small cap working in the data space misuses data collected. Its low share price would plummet to a point it may not recover. Brand strength for a small cap is also imperative to its long-term survival. Especially tech stocks.

Accenture has released a slideshow titled Building Digital Trust.

It covers best practices for data sharing and offers 12 guidelines to building a code of data ethics. It includes governance, transparency, privacy safeguards and respect for someone’s data.

We’ll leave you with a link to this Forbes article, which looks at Blockchain, cybersecurity, cloud computing, automation, AI and data ops and makes you think about all the industries that have moved into data use and how they are disseminating that data.



General Information Only

S3 Consortium Pty Ltd (S3, ‘we’, ‘us’, ‘our’) (CAR No. 433913) is a corporate authorised representative of LeMessurier Securities Pty Ltd (AFSL No. 296877). The information contained in this article is general information and is for informational purposes only. Any advice is general advice only. Any advice contained in this article does not constitute personal advice and S3 has not taken into consideration your personal objectives, financial situation or needs. Please seek your own independent professional advice before making any financial investment decision. Those persons acting upon information contained in this article do so entirely at their own risk.

Conflicts of Interest Notice

S3 and its associated entities may hold investments in companies featured in its articles, including through being paid in the securities of the companies we provide commentary on. We disclose the securities held in relation to a particular company that we provide commentary on. Refer to our Disclosure Policy for information on our self-imposed trading blackouts, hold conditions and de-risking (sell conditions) which seek to mitigate against any potential conflicts of interest.

Publication Notice and Disclaimer

The information contained in this article is current as at the publication date. At the time of publishing, the information contained in this article is based on sources which are available in the public domain that we consider to be reliable, and our own analysis of those sources. The views of the author may not reflect the views of the AFSL holder. Any decision by you to purchase securities in the companies featured in this article should be done so after you have sought your own independent professional advice regarding this information and made your own inquiries as to the validity of any information in this article.

Any forward-looking statements contained in this article are not guarantees or predictions of future performance, and involve known and unknown risks, uncertainties and other factors, many of which are beyond our control, and which may cause actual results or performance of companies featured to differ materially from those expressed in the statements contained in this article. S3 cannot and does not give any assurance that the results or performance expressed or implied by any forward-looking statements contained in this article will actually occur and readers are cautioned not to put undue reliance on forward-looking statements.

This article may include references to our past investing performance. Past performance is not a reliable indicator of our future investing performance.

 

Discover Small Cap
Biotech Stocks

Join thousands of other Investors following our stock commentary for Free

X