Go directly to contents.
Good practices for Service Developers

Using AI responsibly

  1. Start
  2. Introduction to data ethics
  3. Assess the capabilities of your organisation
  4. Organise ethical practices
  5. Get citizens involved
  6. Define a data policy
    1. Well-working AI requires high-quality data
    2. Biases have to be identified by humans
    3. The algorithm determines the quality of AI outputs
      1. A ready-made algorithm is no use
      2. The algorithm is the recipe of an AI system
      3. An accountable algorithm comes from knowing your topic
    4. Big data creates a black box
    5. Citizens have the right to the transparency of information
    6. Challenges of recommender systems in the public sector
  7. Pay attention to security
  8. Checklist

The algorithm determines the quality of AI outputs

A ready-made algorithm is no use

Accountability and ethics are context specific. Standards that were set and ethical issues that were dealt with in an algorithm’s original context may be problems in a new application.

– Publication of the Data & Society research organisation: Algorithmic Accountability: A PrimerOpens in a new window..
Updated: 9/11/2023

The algorithm is the recipe of an AI system

Data is to an information system as flour, sugar and eggs are to a cake, and an algorithm is to system output as a recipe is to baking a cake. You cannot make a cake without ingredients and a recipe, and a system cannot produce anything without data and an algorithm.

An algorithm is the active component of AI in the sense that outputs are processed from data within its framework, with the conditions and features created for it. If an algorithm and its variables are designed carelessly, the outputs are either of poor quality or even harmful.

This means that the rule of thumb of “garbage in, garbage out” alone is not enough to cover all the risks; instead, you also have to understand “quality in, but garbage out with a bad algorithm” – just like baking a cake.

Read about the key problems and solutions of ethics and algorithms in this article in AI & SocietyOpens in a new window..

Updated: 9/11/2023

An accountable algorithm comes from knowing your topic

Each algorithm is different and linked to the objectives of its respective system. For this reason, it is difficult to formulate universal guidelines for building a safe and accountable algorithm other than “know the target phenomenon or population and its features and conditions, and take these into account as objectively as possible in the algorithm’s parameters and their weightings”.

The more complex the target, the more parameters an algorithm has. In some very narrow AIs, there can be just ten or so parameters, whereas for example the GPT3 model, which ChatGPT is based on, is known to have around 175 billion parameters. That alone indicates that the rationale behind the individual outputs of ChatGPT cannot be fully explained or made transparent by human efforts.

In fact, ChatGPT is a typical “black box”, a type of system we’ll discuss more closely on the next page.

Updated: 9/11/2023

Are you satisfied with the content on this page?

Checklist