This training focuses on the more in-depth understanding of the biases that often creep into AI-powered content production. The goal is to train communication and marketing teams who use AI tools to create images, text, and personalized campaigns on how to evaluate the results to avoid stereotypes and cultural simplifications, ensuring accessibility for people with different cognitive and neurodevelopmental needs, while maintaining the authenticity of the product or service that is being promoted.
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Training Marketing & Communication professionals in the inclusive use of AI
Training Software Development professionals in the inclusive integration of AI systems
This training focuses on the responsible design, development, and integration of AI systems. Teams are trained on how to identify and reduce biases in code and data, as well as how to assess the quality and whether the database used to train models is representative of diversity. This training emphasises designing systems that incorporate ethics and fairness-by-design principles from the early stages of development and systems creation, ensuring that they are representative and accessible for people with different cognitive and neurodevelopmental needs.
Training Human Resources teams in the inclusive use of AI
This training focuses on identifying and checking biases that often emerge while using AI tools for job description creation, as well as automated hiring systems, resume evaluations, and performance appraisal software. The goal is to train HR executives on how to apply AI tools responsibly and transparently, as well as evaluation of automated systems to avoid the unintentional exclusion of specific social or demographic groups, ensuring respect over diversity and equal treatment.
Executive training: inclusive management of organizational change following AI tool integration
There is pressure on organizations to adopt AI tools and automate processes so that they remain competitive, all while avoiding the potential negative impacts of fragmented and unregulated use of AI. This training provides people leaders with the skill set to identify their organization’s needs of responsible and inclusive use of AI before they proceed with the strategic adoption of tools. The goal is deepening their understanding of both the benefits and potential risks associated with changing organizational culture. This training supports people in managing positions in handling organizational changes brought by the integration of AI tools, with an emphasis on transparency and trust, while focusing on an inclusive approach.
Algorithmic bias, discrimination, and social bias
AI tools can often reproduce or even reinforce inequality by design. The goal of this training is the in-depth understanding of the ways in which AI tools can produce results that promote inequality and discrimination.
The training is designed to cover the following:
- What is artificial intelligence, what is productive artificial intelligence and what it is not
- Algorithmic bias, discrimination, and bias
- Bias in data and results
- Impact of bias at work. Examples of bias in hiring processes, performance evaluation, report generation
- Hands-on exercises related to the adoption of AI tools and focus on appropriate prompting
AI literacy for all
When referring to AI literacy, we often focus on the technical skills involved in using generative AI, such as writing efficient prompts or selecting from an array of tools and providers. However, we must first understand how AI tools work and what their limitations are to develop accurate criteria on how to use them responsibly and how to evaluate them, a skill applicable to all service providers. The goal of this training is for employees to develop skills that will turn them into informed users, as well as a basic skill set needed in the use of AI.
The training is designed to cover the following:
- What is artificial intelligence, what is productive artificial intelligence and what it is not
- Where is AI used in our specific field of work
- Algorithmic bias, discrimination, and bias
- AI hallucinations
- Privacy and data confidentiality
- Importance of human supervision
- Evaluating tools in relation to their inclusiveness
- Responsible and ethical use of AI
