Artificial Intelligence and Whistleblowing: Can A.I. be useful for Whistleblowing processes?

Abstract

In recent years, there is a constantly growing discussion about the contribution of whistleblowers and the need to protect them. Another rising topic of discussion is the increasing use of Artificial Intelligence (AI), the gains from it, the hardships, the moral dilemmas. This article refers to the development of whistleblower protection in the EU and discusses how AI can be applied in a whistleblowing context.

Keywords

• • Applied Computing ➝ Law
• social and behavioral

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One of the definitions of the term whistleblowing is “the disclosure by organization members (former or current) of illegal, immoral or illegitimate practices under the control of their employers, to persons of organizations that may be able to effect action” [ 4 ].

The phenomenon of individuals coming forward to speak about important issues has a long history, but the term ‘whistleblowing’ came to prominence in the US in the 1960 and early 70’s. Some of the fist law intended to protect whistleblowers emerged in the US. In Europe, the Committee on Legal Affairs and Human Rights of the Parliamentary Assembly of the Council of Europe submitted, in 2009, a report which concluded that although whistleblower Regarding the reporting channels, the Directive follows the three –tiered model [ 11 ], which means that whistleblowers can report internally [ 12 ], within a legal entity, externally to competent authorities [ 13 ] or, as a last resort, they can disclose their information publicly, to the media [ 14 ].

Confidentiality and anonymity are really important for whistleblower protection. When we talk about confidentiality it means that the identity of a whistleblower is known to specific individuals, whereas anonymity means that a whistleblower is not known at all. According to the Directive ‘Member States shall ensure that the identity of the reporting person is not disclosed to anyone beyond the authorized staff members competent to receive or follow up on reports, without the explicit consent of that person. This shall also apply to any other information from which the identity of the reporting person may be directly or indirectly deduced’ [ 15 ]. As far as anonymity is concerned, the Directive lets Member States decide whether organizations and competent authorities are required to accept and follow up on anonymous reports. This is not in line with current best practice. While anonymous disclosures can make it harder to investigate a concern, this should not prevent a concern being taken seriously [ 10 ]. In order to secure the identity of the whistleblower there are hotlines and technology has been a game changer in anonymous whistleblowing, with the advent of the anonymous online drop boxes, which use encryption and other privacy enhancing technologies to obscure the identity of the reporting person. A timely response after a report is also important. According to the Directive, a reporting person is entitled to have their report acknowledged within seven days and receive feedback within three months [ 12 ].

There is not much data about whistleblower reports in Europe. One of the few countries for which there is data is the Netherlands: according to the Dutch Whistleblowers Authority 2018 Annual Report, a total of fifty requests for investigation had been submitted between 2016 and 2018. Of these, 28 were declared inadmissible [ 16 ].

In the US, however, there is an increase in whistleblower reports in the last few years. According to the 2019 US Securities and Exchange Commission Annual Report to Congress on the Whistleblower Program, which provides incentives and protection to whistleblowers, the Commission received more than 5.000 reports, which represents a 74% increase since whistleblower data collection started in 2012 [ 17 ].

The Occupational Safety and Health Administration, a government agency responsible for protecting workers in a wide range of industries, received in 2018 more than 9.500 reports, creating a significant backlog in light of a reduction in the number of investigators. More than 3.000 of those complaints resulted in a full investigation. With such great workload, it is important to have the means to react quickly and in the most appropriate way. This raises the question whether Artificial Intelligence could be of use.

2. ARTIFICIAL INTELLIGENCE FOR

WHISTLEBLOWER PROTECTION

Artificial Intelligence is distinguished in two big general categories. Artificial Narrow Intelligence (ANI), sometimes referred to as Weak AI, that specializes in one area and Artificial General Intelligence (AGI), sometimes referred to as Strong AI, or Human-Level AI. Artificial General Intelligence refers to a computer that is as smart as a human across the board — a machine that can perform any intellectual task that a human being can [ 18 ].

AI is a collection of technologies that combines data, algorithms and computing power. The evolution of Artificial Intelligence has been rapid in recent years with the result that it is at the center of the “Digital Single Market” [ 19 ]. AI can be used to bring the benefits of the technology to society and economy, for citizens, for business development and for services of public interest [ 20 ]. As far as whistleblowing is concerned, thoughts are already being made about the use of Artificial Intelligence in whistleblowing practice [ 21 ]. In the following sections, we elaborate on possibilities of using AI for whistleblowing in three areas: reporting systems, vetting process automation and proactive monitoring.

2.1 AI for whistleblowing reporting systems

Advances in technology are already influencing changes in the way speak up reports are performed. AI can be a technology enabler for whistleblower reporting systems in several ways. Virtual AI agents (chatbots) can assist or entirely handle the submission of whistleblower reports. Chatbots are AI-enabled software applications that can conduct conversations with human users with voice or text [ 22 ]. They use natural language processing (NLP) capabilities to recognize what their human interlocutors say and to formulate responses that try to resemble what a human agent would reply. Chatbots are widely used today in a range of industries, for example for customer care in telecommunications, banking, and healthcare. They are particularly effective as interactive guides of users to structured processes or transactions, such as submitting an issue report to a telecommunications provider, reporting a lost credit card or scheduling a medical appointment. Virtual agents can be helpful for whistleblowers submitting an allegation by interactively providing instructions throughout the process. They can also help reduce the number of incomplete or non-eligible reports by advising users on what are the requirements for a submission and the scope of handled cases. A recent study reports that whistleblowers were more likely to report to an online platform when a virtual agent handled the reports because they believed that it is more efficient and provides greater control while reporting [ 23 ].

AI-based live translation can help increase the accessibility of whistleblowing reporting services across ethnic and cultural communities. Automatic real time translation can enable interactive cross-language reporting through hotlines, i.e. over phone to a human agent or through a chatbot (voice or text). Voice-based hotlines are a very common submission channel for whistleblowers. For example, it is by far the most frequent reporting method (37% of reports in 2019) in corporate ethics compliance systems, as reported by the largest provider of ethics reporting software in the US [ 24 ]. Real-time machine translation is a relatively mature technology, fueled particularly by advances in neural networks and deep learning [ 25 ]. There are commercial offerings of translation-as-a-service by major technology providers which can be used by the developers of whistleblowing applications to allow cross-lingual communication between submitters of whistleblowing reports and handing agents on a 24/7 basis without the huge costs of having stand-by human interpreters for multiple languages [ 23 ]. There are already reallife applications of live translation in other domains, such as communication of teachers with parents of different cultures in middle schools in the US [ 26 ].

2.2 AI for making the vetting process more efficient

As seen from the above-mentioned scandals and a lot of others that see the light all the more often the last years, there is a lot of misconduct. While whistleblower protection evolves, many organizations will engage in some type of whistleblowing investigation, whether it is conducted internally or from an outside agency. It is important for an organization to give the tone from the top and cultivate a culture that promotes ethical behavior and a speak-up culture. Being able to understand the motives of whistleblowers is also important, but vetting and investigating the complaints is critical. To this end, one must have the appropriate auditing tools.

Currently, the impact of AI is mostly discussed in financial audits and is especially pronounced in the area of data acquisition (data extraction, comparison, and validation). This means that AIenabled technology can locate relevant information, extract it from documents, and make it usable for the human auditor, who can devote more time to areas requiring higher-level judgment. For example, AI enables full automation of time-consuming tasks such as payment transaction testing, including extraction of any supporting data for further substantive testing [ 27 ][ 28 ]. The procedural aspect of a whistleblower investigation is more or less the same as any other investigation that corporations are subjected to. What is critical in an internal whistleblower investigation is to determine if the allegation is true [ 29 ]. Whistleblower reports are usually followed by a great amount of data. Sometimes though only a small part of this data may actually be relevant to the action or omission that represents a serious threat or harm. Advanced analytic tools and AI can be used in order to locate and extract critical information from whistleblower reports. Artificial Intelligence in the form of machine learning can group similar documents for faster review. Data sampling is useful to provide insight into larger data. One could distinguish between the use of AI for structured data analytics and its use for analytics over unstructured data [ 30 ]. Structured data is comprised of clearly defined data types whose pattern makes them easily searchable; while unstructured data – “everything else” – is comprised of data that is usually not as easily searchable, including formats like audio, video, and social media postings In structured data analytics, AI and analytics tools can be set up to look for example for transactions that exceed norms, transactions with vague or missing detail, rush requests, unusual cash disbursements, or transactions that circumvent typical approval processes. All these could be characterized as red flags and could direct the investigator towards the person or persons responsible for those transactions. On the other hand, in unstructured data (documents, email, and other messaging) keyword language filters could be used in order to expose use or change in language that may indicate unethical or noncompliant behavior.

2.3 AI for proactive monitoring

Artificial Intelligence can also be used for proactive monitoring, in order to identify potential areas of risk. If for example a certain type of bad behavior is becoming more common in an organization’s industry sector, that particular subject matter could be targeted for routine monitoring using AI and linguistic and analytics tools to flag worrisome language or sentiment in data stores. Analytical tools and predictive software will enable companies to combine whistleblowing data with information from across the business and identify where problems are most likely to occur in the future [ 28 ]. 3.

CONCLUSIONS – FUTURE OUTLOOK

More organized steps were taken just recently in order to better regulate whistleblower protection in the EU and already there are thoughts about the use of AI in whistleblower protection. Even the idea that robots could replace whistleblowers is being explored as a potential future development [ 31 ]. If robots gradually take the place of workers (let’s say in the automotive industry), will there be a time that robots conceive and report wrongdoings? One could argue that the ‘penalisation’ of the whistleblower will no longer exist [ 32 ].

Nevertheless, the potential replacement of whistleblowers by AI raises a lot of questions, some of them being whether a robot will know what to blow the whistle on and if it will be able to follow the three tiered model. Moreover, depending on the country and legislation, whistleblowers can report illegalities, irregularities, wrongdoings or immoral actions. Can a robot know immoral actions? [ 31 ] Scandals as LuxLeaks did not entail illegal acts but obscure legal practices that were considered immoral by society. Will a machine ever be able to report such acts even internally? Artificial Intelligence is a useful tool in detecting wrongdoing. Nevertheless, a fair balance between human actions and the use of AI will always be a crucial aspect. 4.

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