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Job Losses Due To Smart Machines and Roles Of Organisational Decision-Making Structures and Processes
Author: Matuku Mphahlele, PhD
This short write-up seeks to answer the question: What are the disablers to complementarity roles in the labour attrition-Artificial Intelligence intersection and how can these challenges be overcome?
This background seeks to advance elements that underpin the historic context and socio-economic realities in relation to the impact of inequality at workplace and the import of AI skills and technology foreign to African culture and incongruent to Africa’s needs. The inherent historic systemic damages, therefore, stimulate numerous questions. In the endeavour to address the knowledge gap this write-up delves in the discussions at the confluence or intersection of human labour loss and the Artificial Intelligence system.
In the endeavour to understand the historic context of workplace inequality in South Africa, studies are reflective of the legislative measures to redress racial and gender inequality. To cite an example, in the mineral and mining sector (MMS) Africans represent 96% (n=142 082), however at senior management level only 34.3% are Africans compared to 59.0% of Whites, and females constitute only 14.4% of the workforce (Mining Qualifications Authority, 2014). In South Africa, the labour force participation rate for women is 53,0% as compared to 64,4% for men (Stats SA, 2022). Noticeably, labour force participation rate indicates a gap between men and women over the period 2012-2022, Stats SA suggests. These unfavourable discrepancies are prevalent in the private sector, notwithstanding legislative instruments, namely, Skills Development and Employment Equity Acts. This picture underscores elements distinctly embedded as disablers and deterrents in the human developmental space as well as equitable access to Artificial Intelligence (AI) and related technical products.
In the African settings, contemporary scholarly debates argue that Kenya, South Africa and Nigeria employ AI to address health, agriculture, fintech, public transportation, government service delivery, and language translation. However, foreign companies use false African identities in the AI environment to market their products to the exclusive profit of multi-national companies. Furthermore, against the dearth of AI skills, research and development (R&D) as well as incremental dependency on foreign technology the human-AI space is perceived as a form of neo-colonial “date colonialism” and “digital colonialism”, literature suggests. Applications of AI deployed in Africa tend to originate from outside the continent and thus lack contextual relevance, particularly in respect of cultural and infrastructural factors (Oxford Insights & IDRC, 2019).
What characterises Artificial Intelligence (AI)?
An AI system can adapt to new unstructured data in the endeavour achieve predefined desired goals. AI is responsive to fulfilment of desired outcomes. Through simulation of human intelligence AI system employs retained structured data located in the realm of past experiences or memory to unravel complexities that come along with new experiences. The system employs deep learning neural networks that attempt to simulate the behavior of the human brain. AI improves automation, performs huge data analysis, and performs tasks at the expense of human labour. These aspects provoke an inquisitive exploration of the elements distinctly peculiar at the interface of the ‘human-AI’ system, in relation to workplace labour attribution.
Problem: Human attributes are disablers to sustainability of a people-centric governance realm, and a socio-economic friendly human-AI co-existence.
Question: What is the extent of complementarity roles against the uncertainty and complexities of human and AI systems?
The complementarity of humans and AI advances the argument that against the input of huge unstructured data, humans have difficulties to filter and process data into functional knowledge. Efficient utilisation of developmental policy instruments involves complex activities that seek to effectively achieve desirable outcomes. However, human limitations in relation to processing complexities associated with huge computational analysis tend to create a predatory symbiotic relationship with AI, at the expense of income earning power and decision-making structures.
How do data-centric skills premised on AI literacy promote socio-economic development in a knowledge deficit society?
The ability to distinguish the nexus between relevancy and credibility of data, and how to process complex data into functional knowledge-based tools is significantly influenced by the responsive collaborative engagement with multiple key stakeholders. Knowledge of how the AI algorithms work could empower decision-makers to draw the criteria or key performance indicators (KPIs) against which smart goals are achievable in the human-AI system of mutual symbiosis.
Accordingly, promotion of collaborative relationships in relation to equity among the vulnerable population, in the main women, youth and those living with limiting disabilities distinctly advances the argument that “humans need to work with and against the machines” for a transformative ‘win-win’ socio-economic development.
How can humans be competitive against the AI system without functional cooperative skills for mutual co-existence?
It is against the backdrop of the (1) peculiarly high lack of capacity development, and socio-economic inequality; (2) the laissez faire organisational culture inflicted with party-political activism; (3) perceived political abandonment; and (4) inadequate business-community collaborative engagement that a mutually beneficial relationship between the human-AI system remains a huge challenge, literature suggests. At this juncture the discussion seeks to explore the question, is a balanced human-AI system achievable or AI implementation is an exaggeration in our current democratic developmental state? In the context of the historic economic marginalisation of the majority citizenry in South Africa there is a perceived ‘win-lose’ in the current human-AI system. Currently, mutually symbiotic relationships are biased towards the economic empowerment of the political elites, whereas there is a dark cloud impregnated with dashed hopes and despondency among the Historically Disadvantaged South Africans (HDSAs), especially Blacks (Africans, Indians, and Coloureds). The high employee attrition rate is due to ineffective employ of smart machines in non-transformative organisational decision-making structures and processes. Regrettably, (1) trust deficit, (2) non-responsive policy implementation capital, (3) silos decision-making structures, (4) perceived fragmented and bureaucratic operational lacunae, and (5) ineffective monitoring and evaluation of developmental systems and programmes manifest in high employee attrition rate in the human-AI space.
In conclusion, this write-up draws attention to how to redress the complex ecology of power imbalance in the space of the historically fragmented South African societies in relation to equitable access to functional AI instruments and how to alleviate related labour attritions.
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