Big data is becoming a reality. Complex and difficult-to-understand data may be found in a wide range of industries. Big data is a critical component of enterprise services and technology architectures. Data science techniques and methodologies can be applied in many different aspects of the working of companies. In this paper, first, as a background, we provide an overview of knowledge management practices and data analysis strategies and techniques in the daily operations of companies working towards development of AI agents, and the need in particular companies can develop human centric AI solutions; Then, we discuss the basics for cross-disciplinary research, in which we stress the need to re-think development processes of AI services and make them more responsible, and we define research questions to investigate the problem. As the research proposal discusses, companies and public institutions, can create and develop new responsible, ethical, and transparent AI services.

1. Data management and
enterprise architectures
for responsible AI
services
Galena Pisoni[0000-0002-3266-1773]
Bálint Molnár[0000-0001-5015-8883]

2. Introduction
 Machine learning (ML)-based systems and solutions have become “de facto”
standard in our digital lives.
 Bughin et al., in the McKinsey report, estimate an overall economic impact
of 13 trillion dollars, data comparable to economic impacts caused by the
introduction of information technology in the twenty-first century, or the
introduction of steam engines in the nineteenth century (see Marcus et al.).
 AI has become a huge business. Many different forms of the so-called
narrow-AI have found their way into the industry. Examples of narrow-AI
are systems that can read bank checks, tag photos, make restaurant
reservations, schedule hair salon appointments, or provide the open hours of
businesses.
 In this paper, we outline a plan for cross-disciplinary research around AI that
will produce solutions that act in a human-centered way. We outline the
background and the need for such research, and we set the basics for cross-
disciplinary research to stimulate discussion and form an interdisciplinary
consortium with members coming from different backgrounds.

3. Background
There’s the need to ideate new enterprise architecture solutions and perform an enterprise engineering exercise
to define a modern information system structure that integrates various digital and online services of companies,
including new AI services companies want to develop(Pisoni et al.)
Nowadays, there are numerous tools derived from Information and Communication Technologies (ICT) that
can be used to acquire knowledge through process mining, utilizing traditional existing company systems such as
Enterprise Resource Planning and Customer Relationship Management, and applying a set of algorithms from
Data Science. Data can also be collected from social media, Internet of Things (IoT) sensors, e-mail, and instant
messaging. In previous work, we have developed a Data Science toolbox that is available and exploitable
published and proposed in this reference
A combination of existing technologies – both internal and external to the company – can be used as source
systems to extract, prepare, ingest, and then store data according to Pisoni et al.
Large data sets with heterogeneous structures may be analyzed fast using Data Science and modern
information architectures. Through this, one can construct data-intensive workflows for gathering, analyzing,
interpreting, and reviewing the results and can put new business and technical approaches and solutions in place.
Exploratory data analysis gives companies the ability to develop new ways of generating new services, based
on data

4. A call for interdisciplinary research – the big picture

5. A call for
interdisciplinary
research –
research
questions
 RQ1. How should companies develop new innovative human-
centric AI-based services? The aim is to study the innovation
ecosystems through which various actors create and use AI-
based services. The focus is to understand the internal business
processes for AI-based service development for companies,
 RQ2. Which are the knowledge management practices and data
analytics techniques that are currently applied for development
of AI systems? How can these practices become more human
centric, ethical, and responsible? Which steps companies should
take to incorporate these practices into their functioning? This
requires a systematic consideration of ethically aligned design,
sustainability, technology that is based on AI, and human factors
design
 RQ3. Which are the suitable enterprise architecture solutions
supporting and suited for such human centric AI services? The
aim is to study and understand how such architectures should be
set and the wider impact of such platforms in a digitally
transformed society, in which such AI agents are deployed, and
understand agile processes and approaches for business to
develop and deliver such AI platforms and systems

6. A call for
interdisciplinary
research –
additional
reflections
 Which data companies should be using for
developing human-centric AI services as well
as suited AI methods and approaches is another
challenge that companies must solve. Using
big data and AI brings regulation and ethical
implications that need to be properly addressed
in new solutions.
 There are privacy- and security-related
consequences, as well as ethical issues, that are
relevant and important to be considered [14].
 Aiming to demystify opaque and
unaccountable algorithms that have proved to
be systematically biased against women and
minority groups, we want to build on such
previous knowledge on what it means to
provide good development practices for ethical
and responsible AI services and work towards
delivering responsible AI solutions for big
companies

7. Conclusions
 Research as we outline in this paper in the long or short term
will enhance the level of ethics, and inform decision-making for
future AI solutions for companies that will be more ethical,
responsible, and transparent.
 Knowledge regarding data analysis strategies and techniques in
the daily operations of companies working towards the
development of AI agents will be analyzed and enhanced with
new proposals coming from this research.