The Compliance Officer Day Blog accepts contributions from guest authors and members of the E&C community. Enjoy this guest blog post on A.I. and machine learning from Sundar Narayanan. To pitch a story to us, email firstname.lastname@example.org with a summary of your idea.
Ethics awareness has evolved from personal training sessions to animated videos and descriptive policies to digital self-learning modules. However, current methods of enabling ethics in business can be limited in their effectiveness due to the following factors:
- Not all messages about ethics cater to the different educational needs and awareness levels of employees.
- Often times, there are common communication modes used across all regions of any organisation, but not every region should be treated the same.
- Many organisations consider communication as awareness, with little consideration to click rate, view rate, engagement, and measurement of these communications.
With the byte-size attention span of millennials and changing office demographics, it is essential to explore advanced technologies to enable and embed ethics in business. Current (and future) technological advancements like machine learning and artificial intelligence can contribute to influencing ethical decision making, besides creating awareness. Machine learning is the computer’s ability to learn without being explicitly programmed. Machine learning considers the following key factors in transforming ethics in business:
- Past data trends/patterns: Data trends of communications include volume, timing, and forms of communications (emailers, videos, infographics, etc.) and the click rate, view rate and read rates of these messages. Such data trends can be further observed based on demographics, language, gender, and device through which messages are accessed. This information is not just helpful in understanding the reach of the ethics communication in question but also helps determine appropriate modes/models for ethics communication.
- Probabilities: Machine learning considers the probabilities of an event happening or not happening based on dynamic data trends. For example, the probability of a video less than one minute on ethics being viewed by female employees across five states in India at 3:00 pm. These probabilities help define and understand ways to maximise the reach. It also helps in observing unusual trends/probabilities of an event taking place or not.
- Correlating with risk events: Machine learning can be embedded to learn based on dynamic and current data. These data points can include risk events relatable to a circumstance. For instance, more whistleblower concerns of harassment received against an identified individual from a specific department from anonymous reporters, after messages on protection against sexual harassment were viewed by the female employees in the said department. It can be triggered based on multiple possibilities/probabilities of certain risk events, thereby functioning as a deterrent. For instance, it can evolve to observe employee behaviour towards reacting to ethics violations with automated mystery communications at select intervals.
Furthermore, machine learning for enabling ethics in business has the following advantages:
- Helps in measuring efficiency and effectiveness of campaigns/communications.
- Transforms the approach from reactive to proactive.
- Brings deeper insights from the data trends/patterns for management consideration for purposes beyond ethics.
It’s almost certain that above aspects may become reality in a few years from now. In other industries like advertising, social media, and digital media, similar technology and capabilities already exist and play a role in our lives every day. Organisations will increasingly recognise the need for adopting machine learning in ethics evangelisation or in enabling ethics in business considering lean ethics and compliance teams and expanding geographies.
Sundar Narayanan is a forensic accountant from India. He leads the forensic services of SKP Business Consulting LLP. He frequently writes on ethics, anti-corruption and investigation techniques. He can be reached at email@example.com.