Data Ethics: Everything You Need to Know When Assessing Data Ethics Skills

Data Ethics

Data ethics is the field of study that deals with the ethical implications of data collection, storage, use, and sharing. It is a relatively new field, but it is becoming increasingly important as we collect and use more and more data.

Some of the key issues in data ethics include:

Consent: How can we ensure that people have meaningful control over their data?
Privacy: How can we protect people's privacy when collecting and using their data?
Fairness: How can we ensure that data is used in a fair and non-discriminatory way?
Transparency: How can we be more transparent about how we collect, use, and share data?
Accountability: Who is accountable for the ethical use of data?

Data ethics is important because it can help us to avoid the potential harms of data collection and use. These harms can include:

Privacy violations: Data breaches and other privacy violations can expose people's personal information, which can lead to identity theft, fraud, and other harms.
Discrimination: Data can be used to discriminate against people in a variety of ways, such as by denying them jobs, housing, or insurance.
Manipulation: Data can be used to manipulate people's behavior, such as by targeting them with personalized advertising or propaganda.
Surveillance: Data can be used to surveil people, which can violate their privacy and freedom.

There are a number of things that organizations can do to promote data ethics, such as:

Obtaining informed consent: Organizations should obtain informed consent from people before collecting or using their data. This means explaining to people how their data will be used and giving them the opportunity to opt out.
Protecting privacy: Organizations should take steps to protect people's privacy, such as encrypting data and using strong access control measures.
Using data fairly: Organizations should use data in a fair and non-discriminatory way. This means avoiding using data to make decisions that could harm people based on their race, gender, religion, or other protected characteristics.
Being transparent: Organizations should be transparent about how they collect, use, and share data. This includes publishing a privacy policy and providing people with access to

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Scott Crowe, Canva (Lead Recruiter - Data)

How can you accurately assess somebody's technical skills, like the same way across the board, right? We had devised a Tableau-based assessment. So it wasn't like a past/fail. It was kind of like, hey, what do they send us? Did they understand the data or the values that they're showing accurate? Where we'd say, hey, here's the credentials to access the data set. And it just wasn't really a scalable way to assess technical - just administering it, all of it was manual, but the whole process sucked!

Cole Brickley, Avicado (Director Data Science & Business Intelligence)

I wouldn't dream of hiring somebody in a technical role without doing that technical assessment because the number of times where I've had candidates either on paper on the CV, say, I'm a SQL expert or in an interview, saying, I'm brilliant at Excel, I'm brilliant at this. And you actually put them in front of a computer, say, do this task. And some people really struggle. So you have to have that technical assessment.

Mike Yates, The British Psychological Society (Head of Data & Analytics)

I was at WooliesX (Woolworths) and we used Alooba and it was a highly positive experience. We had a large number of candidates. At WooliesX, previously we were quite dependent on the designed test from the team leads. That was quite a manual process. We realised it would take too much time from us. The time saving is great. Even spending 15 minutes per candidate with a manual test would be huge - hours per week, but with Alooba we just see the numbers immediately.

Shen Liu, Logickube (Principal at Logickube)

We get a high flow of applicants, which leads to potentially longer lead times, causing delays in the pipelines which can lead to missing out on good candidates. Alooba supports both speed and quality. The speed to return to candidates gives us a competitive advantage. Alooba provides a higher level of confidence in the people coming through the pipeline with less time spent interviewing unqualified candidates.

Scott Crowe, Canva (Lead Recruiter - Data)

How can you accurately assess somebody's technical skills, like the same way across the board, right? We had devised a Tableau-based assessment. So it wasn't like a past/fail. It was kind of like, hey, what do they send us? Did they understand the data or the values that they're showing accurate? Where we'd say, hey, here's the credentials to access the data set. And it just wasn't really a scalable way to assess technical - just administering it, all of it was manual, but the whole process sucked!

Cole Brickley, Avicado (Director Data Science & Business Intelligence)

I wouldn't dream of hiring somebody in a technical role without doing that technical assessment because the number of times where I've had candidates either on paper on the CV, say, I'm a SQL expert or in an interview, saying, I'm brilliant at Excel, I'm brilliant at this. And you actually put them in front of a computer, say, do this task. And some people really struggle. So you have to have that technical assessment.

Mike Yates, The British Psychological Society (Head of Data & Analytics)

I was at WooliesX (Woolworths) and we used Alooba and it was a highly positive experience. We had a large number of candidates. At WooliesX, previously we were quite dependent on the designed test from the team leads. That was quite a manual process. We realised it would take too much time from us. The time saving is great. Even spending 15 minutes per candidate with a manual test would be huge - hours per week, but with Alooba we just see the numbers immediately.

Shen Liu, Logickube (Principal at Logickube)