Government agencies across South Africa are increasingly counting on digital tools evaluate public programs and monitor their performance. This is a component of wider public sector reforms. Its goals are to enhance accountability, reply to audit pressures, and manage large-scale programs with limited staff and budgets.
Here is an example. National departments that oversee housing provision, social grants or infrastructure development depend on digital performance systems quite than regular paper-based reports. Dashboards – a approach to display visual data in a single place – provide near real-time service delivery updates.
Another reason is the usage of platforms that collect mobile data. These allow frontline officials and contractors to upload information directly from the sphere.
Both examples lend themselves to the usage of artificial intelligence (AI) to process large data sets and generate insights that might previously have taken months to research.
This shift occurs incessantly shown as a step forward for accountability and efficiency in the general public sector.
I’m a political scientist with a specific interest in monitoring and evaluating government programs. My current one Research shows a worrying trend that the embrace of technology is going on much faster than the moral and governance frameworks designed to manage it.
In the cases I examined, digital tools were already embedded in routine monitoring and evaluation processes. However, there have been no clear standards for his or her use.
This poses risks of surveillance, exclusion, data misuse and poor skilled judgment. These risks will not be abstract. They shape how residents experience the state, how their data is handled and whose votes ultimately count in political decisions.
When technology surpasses politics
Public sector evaluation involves evaluating government programs and policies. It determines whether:
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public resources are used effectively
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Programs achieve their intended results
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Citizens can hold the state accountable for its performance.
Traditionally, these assessments have relied on face-to-face interactions between communities, assessors, the federal government and others. They included qualitative methods that allowed for nuance, explanation and trust constructing.
Digital tools have modified this.
As a part of my research, I interviewed experts from governments, NGOs, academia, skilled associations and personal consulting firms. I discovered a consistent concern across the board. Digital systems are sometimes introduced without ethical guidance tailored to evaluation practice.
Ethical guidelines would offer clear, practical rules for the usage of digital tools in evaluations. For example, if dashboards or automated data evaluation are used, evaluators should explain within the instructions how data is generated, who has access to it, and the way the outcomes may impact the communities being evaluated. It also needs to prevent digital systems from getting used to watch individuals without consent or to guage programs in a way that ignores context.
South Africa Personal Data Protection Act provides a general legal framework for data protection. However, it doesn’t address the particular ethical dilemmas that arise when assessment is automated, cloud-based and algorithmically mediated.
The result’s that reviewers often must navigate complex ethical terrain without clear standards. This forces institutions to depend on precedents, informal habits, past practices, and software standards.
Monitoring clause and data misuse
Digital platforms enable the gathering of huge amounts of knowledge. Once data is uploaded to cloud-based systems or third-party platforms, control over its storage, reuse, and distribution often passes from evaluators to others.
Several evaluators described situations through which data they collected on behalf of presidency agencies was later reused by ministries or other government agencies. This happened without the specific knowledge of the participants. Consent processes in digital environments are sometimes limited to a single click.
Examples of other uses included other forms of research, reporting, or institutional monitoring.
One of the moral risks that arose from the research was the usage of this data for surveillance. This is the use of knowledge to watch individuals, communities or frontline staff.
Digital exclusion and invisible voices
Digital evaluation tools are sometimes presented as expanding reach and participation. However, in practice they’ll exclude already marginalized groups. Communities with limited web access, low digital literacy, language barriers, or unreliable infrastructure are less prone to fully take part in digital assessments.
Automated tools have limitations. For example, they could find it difficult to process multilingual data, local accents, or culturally specific expressions. This results in partial or distorted representations of lived experience. The reviewers in my study saw this in practice.
This exclusion has serious consequences, especially in an unequal country like South Africa. Evaluations that rely heavily on digital tools could find urban, connected populations and render rural or informal communities statistically invisible.
This shouldn’t be only a technical limitation. It shapes which needs are recognized and whose experiences are incorporated into political decisions. If evaluation data underrepresents those most in danger, public programs may appear simpler than they’re. This obscures structural defects quite than correcting them.
In my study, some evaluations reported positive performance trends, although the evaluators noted gaps in data collection.
Algorithms will not be neutral
Evaluators also expressed concerns concerning the growing authority given to algorithmic results. Dashboards, automated reports, and AI-driven analytics are sometimes seen because the true picture. This happens even once they conflict with subject-specific knowledge or contextual understanding.
For example, dashboards can show a goal as being on course. But in a site visit example, evaluators may find deficiencies or dissatisfaction.
Several participants reported pressure from donors or institutions to depend on the evaluation of the numbers.
Still, algorithms reflect the assumptions, data sets, and priorities embedded of their design. If used uncritically, they’ll reproduce bias, oversimplify social dynamics, and ignore qualitative insights.
When digital systems dictate how data should be collected, analyzed and reported, reviewers risk becoming technicians quite than independent experts making their judgments.
Why Africa needs context-sensitive ethics
Across Africa, national strategies and policies on digital technologies often draw heavily from international frameworks. These are developed in very different contexts. Global principles AI ethics And Data management provide useful clues. However, they don’t adequately address the fact of inequality, historical mistrust and unequal digital access across much of Africa's public sector.
My research argues that ethical governance for digital assessment should be context-sensitive. Standards must address:
Ethical framework conditions should be anchored within the design phase of digital systems.
