Data

Ethics and Security of Artificial Intelligence

ethique et sécurité de l'intelligence artificielle

Duration

1 Day

Languages

French - English

Trainer(s)

Srikanth RAMANOUDJAME - Lead Digital Future Astrakhan

With the rise of Big Data ecosystem technologies (both in terms of storage and visualization), Artificial Intelligence (AI), and more precisely Machine Learning techniques, have rapidly become widely available and have been applied in many industries. AI modifies, optimizes, or transforms some industries, whether in marketing, e-commerce, banking, retail, or energy. Many of them are moving from reactive business logic to predictive and proactive logic.

In this program, you will see the main applications and impacts of artificial intelligence. You will also address the ethical and security issues arising from the operation and deployment of artificial intelligence technologies, which can result, if poorly perceived, in reluctance among employees. And the consequent value of regulations and measures to manage these risks. 

Target audience

  • All Profiles

Prerequisites

  • General knowledge about Data challenges and domains

Course Delivery

On site,
in your offices

Remote,
via Teams

Podcasts

Training Program

AI – from its inception to modern applications 

  • A little bit of history
  • A pragmatic definition
  • AI today

Main applications, impacts, and transformation levers

  • Added value and transformations
  • Business contexts and types of AI projects
  • Which enablers for which types of projects?

Issues related to models​

  • Development​
  • Implementation​
  • Usability and maintainability: model performance, neural network hacking​

Ethical issues​

  • Data Collection and Data Exploitation Issues ​
    • What are the risks?​
    • What are the corresponding GDPR regulations?​
  • Questions related to machine learning techniques ​
    • Summary of cognitive biases​
    • Consequences of automating certain processes and tasks​

Security issues​

  • Data Quality and Data Management,​
  • Trust and explainability ​
    • Demystifying the black box​
    • “Explainable” AI​
  • Regulations and norms​
  • Artificial intelligence hacking​

Retrospective – Q&A

Request a quote