@ -30,4 +30,4 @@ Vul de volgende tabel in (vul je eigen onderwerp in, indien nodig):
Uitstekend | Adequaat | Vereist verbetering
--- | --- | -- |
Men kon voldoende databronnen vinden, deze juist opslaan en hier de juiste inzichten aan ontlenen voor alle probleemstellingen. | Sommige aspecten van de oplssing zijn niet concreet, de data opslag is niet gedefinieerd, tenminste 2 van de probleemstellingen zijn besproken. | Enkel onderdelen vand e oplossing zijn beschreven, slechts een van de probleemstellingen is besproken.
Men kon voldoende databronnen vinden, deze juist opslaan en hier de juiste inzichten aan ontlenen voor alle probleemstellingen. | Sommige aspecten van de oplssing zijn niet concreet, de data opslag is niet gedefinieerd, tenminste 2 van de probleemstellingen zijn besproken. | Enkele onderdelen van de oplossing zijn beschreven, slechts een van de probleemstellingen is besproken.
@ -12,7 +12,7 @@ Market trends tell us that by 2022, 1-in-3 large organizations will buy and sell
Trends also indicate that we will create and consume over [180 zettabytes](https://www.statista.com/statistics/871513/worldwide-data-created/) of data by 2025. As **Data Scientists**, this gives us unprecedented levels of access to personal data. This means we can build behavioral profiles of users and influence decision-making in ways that create an [illusion of free choice](https://www.datasciencecentral.com/profiles/blogs/the-illusion-of-choice) while potentially nudging users towards outcomes we prefer. It also raises broader questions on data privacy and user protections.
Data ethics are now _necessary guardrails_ for data science and engineering, helping us minimize potential harms and unintended consequences from our data-driven actions. The [Gartner Hype Cycle for AI](https://www.gartner.com/smarterwithgartner/2-megatrends-dominate-the-gartner-hype-cycle-for-artificial-intelligence-2020/) identifies relevant trends in digital ethics, responsible AI,and AI governance as key drivers for larger megatrends around _democratization_ and _industrialization_ of AI.
Data ethics are now _necessary guardrails_ for data science and engineering, helping us minimize potential harms and unintended consequences from our data-driven actions. The [Gartner Hype Cycle for AI](https://www.gartner.com/smarterwithgartner/2-megatrends-dominate-the-gartner-hype-cycle-for-artificial-intelligence-2020/) identifies relevant trends in digital ethics, responsible AI,and AI governance as key drivers for larger megatrends around _democratization_ and _industrialization_ of AI.
@ -84,7 +84,7 @@ The moral questions we need to ask are:
#### 2.2 Informed Consent
[Informed consent](https://legaldictionary.net/informed-consent/) defines the act of users agreeing to an action (like data collection) with a _full understanding_ of relevant facts including the purpose, potential risks,and alternatives.
[Informed consent](https://legaldictionary.net/informed-consent/) defines the act of users agreeing to an action (like data collection) with a _full understanding_ of relevant facts including the purpose, potential risks,and alternatives.
Questions to explore here are:
* Did the user (data subject) give permission for data capture and usage?
@ -242,7 +242,7 @@ Examples of data protection and privacy regulations:
### 4. Ethics Culture
Note that there remains an intangible gap between _compliance_ (doing enough to meet "the letter of the law") and addressing [systemic issues](https://www.coursera.org/learn/data-science-ethics/home/week/4) (like ossification, information asymmetry,and distributional unfairness) that can speed up the weaponization of AI.
Note that there remains an intangible gap between _compliance_ (doing enough to meet "the letter of the law") and addressing [systemic issues](https://www.coursera.org/learn/data-science-ethics/home/week/4) (like ossification, information asymmetry,and distributional unfairness) that can speed up the weaponization of AI.
The latter requires [collaborative approaches to defining ethics cultures](https://towardsdatascience.com/why-ai-ethics-requires-a-culture-driven-approach-26f451afa29f) that build emotional connections and consistent shared values _across organizations_ in the industry. This calls for more [formalized data ethics cultures](https://www.codeforamerica.org/news/formalizing-an-ethical-data-culture/) in organizations - allowing _anyone_ to [pull the Andon cord](https://en.wikipedia.org/wiki/Andon_(manufacturing)) (to raise ethics concerns early in the process) and making _ethical assessments_ (e.g., in hiring) a core criteria team formation in AI projects.
Jasmine Greenaway
3 years ago
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GitHub