BDVA Algorithmic Transparency Survey

1.Are you a BDA member?

Yes

No

2.Is your organization

an SME (small & medium enterprise)

a corporate (industry, media, services)

a corporate (consulting)

an RTO (research & technology organization)

a governmental body

Other (please specify)

Other concepts
Alongside 'transparency', we find many notions for other concepts. Some of them may be more relevant to you than 'transparency'.

Value chain
Typically, data is processed in value chains. Every part of the chain may use algorithms for the processing. So it is not only the end user that may require transparency.

5.Can you identify examples where end-user transparency requirements create a 'waterfall effect', where they lead to transparency requirements earlier in the value chain?

6.Can you give examples of situations where a value chain partner requested transparency from your side?

Existing initiatives
There is a couple of initiatives that already (partly) address transparency. A brief summary:

France

Digital Republic Bill
TransAlgo

Netherlands

VWData
Ministry of Economic Affairs (benchmark)

Europe

H2020 projects like R3COP
AI for People (forum)
Working group on legal questions of robotics

Outside EU

AI R&D Guidelines (Japan)
Whitehouse report on future of AI
IEEE ethically aligned design

7.Do you know of other existing (national) initiatives that address algorithmic transparency?

8.How do you think the topic is best addressed at EU level?

In an H2020/FP9 project

As an added aspect in other topics

On the EU policy level

Not at all

Other (please specify)

Legal aspects
One driver for Algorithmic Transparency is the GDPR, for instance article 12: provide information on processing in transparent form, meaningful information; and article 22 on Automated individual decision-making, including profiling

9.Would your organization have uncertainty regarding legal aspects of transparency, and if yes, which ones?

Which minimum level of algorithmic transparency is required

What internal systems and processes are in scope

Whether our organization's processes can be seen as 'individual decision making' or profiling

None

Other (please specify)

Technical aspects

Algorithmic transparency requirements raise many technical issues. Although transparency can help to improve quality, there are limits because of complexity of algorithms. There may also be risks in terms of vulnerability and IPR. The question is also what timing we should adopt when we want to standardize aspects of transparency.

10.What do you think are the main technical challenges?

To include contextual information (like training data, design & test process)

To handle complexity (interpreting machine learning, or dependencies in processing chains)

To protect IPR

Other (please specify)

11.Which parts of an algorithm should be transparent?

The algorithm design process (requirements, goals, team)

The algorithm logic itself

The training data collection process

The training data description

The input data that is used in a specific situation

Other (please specify)

12.Do you think that algorithmic transparency should be standardized?

No, this is not needed

Yes, in an industry bottom-up approach (eg, 'best available techniques')

Yes, in a more formal way, but not now

Yes, in a more formal way, and we should start now

Other (please specify)

Business

There may be positive and negative business impacts.

13.Which business aspects of algorithmic transparency do you expect in your business?

An additional service proposition (toprovide auditing or certification services)

Being transparent can be part of the marketing mix or our company profile (eg, Corporate Social Responsibility)

Better quality: transparency uncovers potential flaws that would otherwice go unnoticed

There will be costs related to certification, compliance, standardization, ...

Profiling or customer segmentation becomes very difficult or will create adverse effects

There can be liabilities when transparency makes shortcomings visible

Other (please specify)

Societal implications

Transparency does not automatically create trust. And the knowledge level of the audience to whom transparency is communicated matters a lot. Expectations and requirements from society influences businesses and technology, and the other way round.

14.What is the relevance of algorithmic transparency for the adoption of AI / machine learning in society?

Highest importance

Important

Not very important

Not important

15.What would be the biggest societal risks when transparency is not addressed in ways that are understandable for the end users?

People becoming algorithm adverse (not trusting anything anymore)

People trust AI/ML too much, and ignore common wisdom or safety

Growth of the digital divide; people who know how to work with algorithms, and people who are clueless

Not a real risk

Other (please specify)