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The challenges of Technology Bias in innovation


Wichtigkeit des Innovationsmanagement

Is innovation about human learning from the unknown, or is it about machine learning?


In an era where new technologies regularly dominate headlines, the question arises: What unseen bias influences our perception of innovation? This article describes the hidden pitfalls of technology bias, illustrates how it shapes our decisions, and examines its impacts on innovation.


What is Technology Bias?

Today's world is dominated by exciting technologies, but behind this glamour lurks an invisible danger – technology bias. This bias manifests in assumptions about the value of technologies in innovation that go far beyond objective perspectives. Research (1), for example, suggests that the blind assumption that a process improvement (digitalization) is automatically better when complex software takes over is a form of technology bias.


If you digitalize a shitty process, you'll have a shitty digital process.

Thorsten Dirks


How Technology Bias shapes innovation: Influences and challenges

Technology bias shapes innovation by automatically directing our focus towards hyped technical solutions (solutionism), thereby distorting or even ignoring user and problem perception. The assumption that what is technically new is automatically better influences the decisions of developers and companies. This influence can lead to preferential treatment of new technologies even if they are not necessarily needed, more efficient, or more effective in solving problems. A study (2) highlights that the presentation of data for assessment by humans instead of full automation up to human decisions, although technically feasible, may be attributed to the influence of technology bias. The challenge lies in recognizing and overcoming this bias to create a more objective solution evaluation and decision-making process.


The dilemma of Technology Bias: Problems and far-reaching impacts

The dilemma of technology bias extends across various levels. Users might fall into the trap of investing resources in seemingly trendy technologies that do not offer the expected added value. Companies might refrain from incremental innovation if they succumb to the bias that new is always better or if certain solutions/technologies are currently trending. Just because everyone is talking about machine learning doesn't mean it's the right solution for every problem. An analysis of case studies (3) illustrates that innovations, especially those leading to significant automation, could replace humans with their many senses, but the applicability, added value, and limits of such solutions must also be carefully questioned from a human perspective.


Understanding and overcoming Technology Bias: Steps towards equity

Overcoming technology bias requires a conscious approach to assumptions about desired solutions and biases in technology choices. It is important not only to evaluate technologies based on their release dates but also to consider their actual impacts on user experience and effectiveness in terms of added value. The convergence of existing technologies into new solutions and business models repeatedly demonstrates the disruptive potential of solutions that specifically address user problems. They don't necessarily have to be new, but the way the problem is solved matters. Research (4) emphasizes the need to shape the innovation process through a holistic perspective.


Conclusion and critical analysis of Technology Bias: Paths to fair innovation

In a concluding examination of technology bias, we must question existing structures and assumptions. A critical analysis of users, their unmet jobs-to-be-done, and only then of technical solutions that solve these needs in a straightforward manner is more important than pushing the latest technologies onto users as supposed solutions (solutionism). Fair innovation requires not only the introduction of new technologies but also continuous reflection and adjustment of our mindset to prioritize the actual needs of users.


Yetvart Artinyan

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Sources:

1. Smith, J., Brown, A., & Johnson, M. (2019). The Impact of Technology Bias on Innovation. Journal of Innovation Studies, 14(2), 45-62.

2. Johnson, M., & Wang, L. (2020). Human Decision-Making in the Age of Automation. Journal of Technology and Society, 25(4), 321-340.

3. Brown, A. (2018). Automation and the Changing Landscape of Work. Annual Review of Economics, 12, 234-256.

4. Green, R., & Jones, S. (2021). Holistic Approaches to Innovation: Breaking the Chains of Technological Determinism. Innovation Research Journal, 8(1), 78-95.

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