Artificial Intelligence

• Process Automation
  offloads repetitive tasks to machines and speeds up the execution
• Information extraction
  automatically extracts knowledge from a large amount of unstructured data
• Anomaly detection
  allows focusing effort on the most suspicious cases

• Comprehensive checks
  ensure compliance with regulations

For (ethical) principles to become actionable, they must be contextualised into specific guidelines for front-line staff. This way organisations may be able to identify the ethical implications of their AI solutions, and the relevant principles that should be taken into account when designing and operationalising AI models, allowing for robust mitigation of ethical risks.

Organisations also must monitor the regulatory environment in which they operate and understand how emerging regulations will shape future business practices.

There are times when it's needed to explain why a particular AI model made a particular decision. A lack of interpretability in AI decisions is not only frustrating for end-users or customers, but can also expose an organisation to operational, reputational, and financial risks.

To instill trust in AI systems, people must be enabled to look “under the hood” at their underlying models, explore the data used to train them, expose the reasoning behind each decision, and provide coherent explanations to all stakeholders in a timely manner.

To be effective and reliable, AI systems need to be:
resilient: next-generation AI systems are likely to be increasingly “self-aware,” with a built-in ability to detect and correct faults and inaccurate or unethical decisions.

secure: the potentially catastrophic outcomes of AI data or systems being compromised or “hijacked” make it imperative to build security into the AI development process from the start, being sure to cover all AI systems, data, and communications.

safe: above all, though, AI systems must be safe for the people whose lives they affect, whether they are users of AI or the subjects of AI-enabled decisions.
 

People perceive bias through the subjective lens of fairness — a social construct with strong local nuances and many different and even conflicting definitions. Therefore it’s impossible for every decision to be fair to all parties, whether AI is involved or not.

But it is possible to tune AI systems to mitigate bias and enable decisions that are as fair as possible and adhere to an organisation’s corporate code of ethics, as well as following anti-discrimination regulations.

In each case, establishing fairness requires businesses to choose their level of comfort in the choices they make, and balance these against the associated costs and wider impacts, which might be negative for some stakeholders.

• Support operation suffered from a slow incident resolution, high cost, and poor customer experience
• Our solution provides:
  • identified pain points
  • identified systemic issues
  • improved prioritization

• Every year, significant number of EU agricultural subsidies are involved in various irregularities
• Our solution makes it possible to:
  • verify declaration from satellite
  • detect agricultural operations
  • detect statistical anomalies

• Client files are often unstructured and require manual processing to extract contained information
• Our solution provides:
  • minimized manual work
  • extraction of important data
  • smart document routing

• Differences between unsynchronized accounting records (breaks) have to be manually classified and fixed
• Our solution makes it possible to:
  • identify and fix breaks
  • analyze each break class