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AI System Engineering—Key Challenges and Lessons Learned^{ †}

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## Abstract

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## 1. Introduction

**Hurdles from Current Machine Learning Paradigms**, see Section 2. These modelling and system development steps are made much more challenging by hurdles resulting from current machine learning paradigms. Such hurdles result from limitations of nowadays theoretical foundations in statistical learning theory and peculiarities or shortcomings of today’s deep learning methods.- −
- Theory-practice gap in machine learning with impact on reproducibilty and stability;
- −
- Lack of uniqueness of internal configuration of deep learning models with impact on reproducibility, transparancy and interpretability;
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- Lack of confidence measure of deep learning models with impact on trustworthiness and interpretability;
- −
- Lack of control of high-dimensionality effects of deep learning model with impact on stability, integrity and interpretability.

**Key Challenges of AI Model Lifecycle**, see Section 3. The development of data-driven AI models and software systems therefore faces novel challenges at all stages of the AI model and AI system lifecycle, which arise along transforming data to learning models in the design and training phase, particularly.- −
- Data challenge to fuel the learning models with sufficiently representative data or to otherwise compensate for their lack, as for example by means of data conditioning techniques like data augmentation;
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- Information fusion challenge to incorporate constraints or knowledge available in different knowledge representation;
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- Model integrity and stability challenge due to unstable performance profiles triggered by small variations in the implementation or input data (adversarial noise);
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- Security and confidentiality to shield machine learning driven systems from espionage or adversarial interventions;
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- Interpretability and transparancy challenge to decode the ambiguities of hidden implicit knowledge representation of distributed neural parametrization;
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- Trust challenge to consider ethical aspects as a matter of principle, for example, to ensure correct behavior even in case of a possible malfunction or failure.

**Key Challenges of AI System Lifecycle**, see Section 4. Once a proof of concept of a data-driven solution to a machine learning problem has been tackled by means of sufficient data and appropriate learning models, requirements beyond the proper machine learning performance criteria have to be taken into account to come up with a software system for a target computational platform intended to operate in a target operational environment. Key challenges arise from application specific requirements:- −
- Deployment challenge and computational resource constraints, for example, on embedded systems or edge hardware;
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- Data and software quality;
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- Model validation and system verification including testing, debugging and documentation, for example, certification and regulation challenges resulting from highly regulated target domains such as in a bio-medical laboratory setting.

#### Outline and Structure

- Overview of challenges and analysis
- Outline of approaches from selected ongoing research projects
- (1)
- Automated and Continuous Data Quality Assurance (see Section 5.1)
- (2)
- Domain Adaptation Approach for Tackling Deviating Data Characteristics at Training and Test Time (see Section 5.2)
- (3)
- Hybrid Model Design for Improving Model Accuracy (see Section 5.3)
- (4)
- Interpretability by Correction Model Approach (see Section 5.4)
- (5)
- Software Quality by Automated Code Analysis and Documentation Generation (see Section 5.5)
- (6)
- The ALOHA Toolchain for Embedded Platforms (see Section 5.6)
- (7)
- Confidentiality-Preserving Transfer Learning (see Section 5.7)
- (8)
- Human AI Teaming as Key to Human Centered AI (see Section 5.8)

## 2. Hurdles from Current Machine Learning Paradigms

#### 2.1. Theory-Practice Gap in Machine Learning

#### 2.2. Lack of Uniqueness of Internal Configuration

#### 2.3. Lack of Confidence Measure

#### 2.4. Lack of Control of High-Dimensionality Effects

## 3. Key Challenges of AI Model Lifecycle

#### 3.1. Data Challenge: Data Augmentation with Pitfalls

#### 3.2. Information Fusion Challenge

- Current deep learning models cannot capture the fully semantic knowledge of the multimodal data. Although attention mechanisms can be used to mitigate these problems partly, they work implicitly and cannot be actively controlled. In this context the combination of deep learning with semantic fusion and reasoning strategies are promising approaches [39].
- In contrast to the widespread use of convenient and effective knowledge transfer strategies in the image and language domain, similar methods are not yet available for audio or video data, not to mention other fields of applications for example, in manufacturing.
- The situation is worsened when it comes to dynamically changing data with shifts in its distribution. The traditional method of deep learning for adopting to dynamic multimodal data is to train a new model when the data distribution changes. This, however, takes too much time and is therefore not feasible in many applications. A promising approach is the combination with transfer learning techniques, which aim to handle deviating distributions as outlined in References [40,41]. See also Section 2.1.

#### 3.3. Model Integrity and Stability Challenge

#### 3.4. Security and Confidentiality Challenge

#### 3.5. Interpretability Challenge

#### 3.6. Trust Challenge

- in terms of high level ethical guidelines (e.g., ethics boards such as algorithmwatch.org (https://algorithmwatch.org/en/project/ai-ethics-guidelines-global-inventory/), EU’s Draft Ethics Guidelines (https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai));
- in terms of regulatory postulates for current AI systems regarding for example, transparency (working groups on standardization, for example, ISO/IEC JTC 1/SC 42 on artificial intelligence (https://www.iso.org/committee/6794475/x/catalogue/p/0/u/1/w/0/d/0));
- in terms of trust modelling approaches (e.g., multi-agent systems community [76]).

## 4. Key Challenges of AI System Lifecycle

#### 4.1. Deployment Challenge and Computational Resource Constraints

#### 4.2. Data and Software Quality

#### 4.2.1. Data Quality Assurance Challenge

- Missing data is a prevalent problem in data sets. In industrial use cases, faulty sensors or errors during data integration are common causes for systematically missing values. Historically, a lot of research into missing data comes from the social sciences, especially with respect to survey data, whereas little research work deals with industrial missing data [24]. In terms of missing data handling, it is distinguished between deletion methods (where records with missing values are simply not used), and imputation methods, where missing values are replaced with estimated values for a specific analysis [24]. Little & Rubin [92] state that “the idea of imputation is both seductive and dangerous”, pointing out the fact that the imputed data is pretended to be truly complete, but might have substantial bias that impairs inference. For example, the common practice of replacing missing values with the mean of the respective variable (known as mean substitution) clearly disturbs the variance of the respective variable as well as correlations to other variables. A more sophisticated statistical approach as investigated in Reference [24] is multiple imputation, where each missing value is replaced with a set of plausible values to represent the uncertainty caused by the imputation and to decrease the bias in downstream prediction tasks. In a follow-up research, also the integration of knowledge about missing data pattern is investigated.
- Semantic shift (also: semantic change, semantic drift) is a term originally stemming from linguistics and describes the evolution of word meaning over time, which can have different triggers and development [93]. In the context of data quality, semantic shift is defined as the circumstance when “the meaning of data evolves depending on contextual factors” [94]. Consequently, when these factors are modeled accordingly (e.g., described with rules), it is possible to handle semantic shift even in very complex environments as outlined in Reference [94]. While the most common ways to overcome semantic shift are rule-based approaches, more sophisticated approaches take into account the semantics of the data to reach a higher degree of automation. Example information about contextual knowledge are the respective sensor or machine with which the data is collected [94].
- Duplicate data describes the issue that one real-world entity has more than one representation in an information system [95,96,97,98]. This subtopic of data quality is also commonly referred to as entity resolution, redundancy detection, record linkage, record matching, or data merging [96]. Specifically, the detection of approximate duplicates has been researched intensively over the last decades [99].

#### 4.2.2. Software Quality: Configuration Maintenance Challenge

## 5. Approaches, In-Progress Research and Lessons Learned

- (1)
- Automated and Continuous Data Quality Assurance, see Section 5.1;
- (2)
- Domain Adaptation Approach for Tackling Deviating Data Characteristics at Training and Test Time, see Section 5.2;
- (3)
- Hybrid Model Design for Improving Model Accuracy, see Section 5.3;
- (4)
- Interpretability by Correction Model Approach, see Section 5.4;
- (5)
- Software Quality by Automated Code Analysis and Documentation Generation, see Section 5.5;
- (6)
- The ALOHA Toolchain for Embedded Platforms, see Section 5.6;
- (7)
- Confidentiality-Preserving Transfer Learning, see Section 5.7;
- (8)
- Human AI Teaming as Key to Human Centered AI, see Section 5.8.

#### 5.1. Approach 1 on Automated and Continuous Data Quality Assurance

**Lesson Learned:**In the literature, data quality is typically defined with the fitness for use principle, which illustrates the high contextual dependency of the topic [91,106]. Thus, one important lesson learned is the need for more research into domain-specific approaches into data quality, which are at the same time suitable for automation [79]. An example from our ongoing research is the data quality tool DQ-MeeRKat (https://github.com/lisehr/dq-meerkat), which implements the novel concept of “reference data profiles” for automated data quality monitoring. Reference data profiles serve as quasi-gold-standard to automatically verify the quality of modified (i.e., inserted, updated, deleted) data. On the one hand, reference data profiles can be learned automatically and therefore require less human effort than rule-based approaches, and on the other hand (ii) they are adjusted to the respective data to be monitored and can therefore considered context-dependent.

#### 5.2. Approach 2 on Domain Adaptation Approach for Tackling Deviating Data Characteristics at Training and Test Time

**Lesson Learned:**It is interesting that moment-based probability distance measure are The weakest among those utilized in the machine learning and, in particular, domain adaptation. Weak in this setting means that convergence by the stronger distance measures entails convergence of the weaker. Our lesson learned is that a weaker distance measure can be more robust than stronger distance measures. At the first glance, this observation might appear counter-intuitive. However, at a second look, it becomes intuitive that the minimization of stronger distance measures are more prone to the effect of negative transfer [115], that is, the adaptation of source-specific information not present in the target domain. Further evidence can be found in the area of generative adversarial networks where the alignment of distributions by strong probability metrics can cause problems of mode collapse which can be mitigated by choosing weaker similarity concepts [116]. Thus, it is better to abandon stronger concepts of similarity in favor of weaker ones and to use stronger concepts only if they can be justified.

#### 5.3. Approach 3 on Hybrid Model Design for Improving Model Accuracy by Integrating Expert Hints in Biomedical Diagnostics

**Lesson Learned:**First, it is crucial to rely on expert knowledge when it comes to data augmentation strategies. This becomes more important the more complex the data is (high number of cores and overlapping cores). Less complex images do not necessarily benefit from data augmentation. Second, by introducing so-called localization units the network is able to gain the ability to exactly localize anomalies in terms of genomic breakpoints despite never experiencing their exact location during training. In this way we have learned that localization and attention units can be used to significantly ease the effort of annotating data.

#### 5.4. Approach 4 on Interpretability by Correction Model Approach

**Lesson Learned:**This approach is work in progress and will be tackled in detail in the upcoming Austrian FFG research project inAIco. As lesson learned we appreciate the BAPC approach as result of interdisciplinary research at the intersection of mathematics, machine learning and model predictive control. We expect that the approach generally only works for small AI corrections. It must be possible to formulate conditions about the size (i.e., smallness) of the AI correction under which the approach will work in any case. However, it is an advantage of our approach that interpretability does not depend on human understanding (see the discussion in References [62,64]). An important aspect is its mathematical rigidity, which avoids the accusation of quasi-scientificity (see Reference [130]).

#### 5.5. Approach 5 on Software Quality by Code Analysis and Automated Documentation

**Lesson Learned:**Keeping documentation up to date is essential for the maintainability of frequently updated software and to minimize the risk of technical debt due to the entanglement of data and sub-components of machine learning systems. The lesson learned is that for this problem also machine learning can be utilized when it comes to establishing rules for detecting and classifying comments (accuracy of >95%) and integrating them when generating readable documentation.

#### 5.6. Approach 6 on the ALOHA Toolchain for Embedded AI Platforms

- (Step 1) algorithm selection,
- (Step 2) application partitioning and mapping, and
- (Step 3) deployment on target hardware.

**Lesson Learned:**Following the standard training procedure deep models tend to be oversized. This research shows that some of the CNN layers are operating in a static or close-to-static mode, enabling the permanent pruning of the redundant kernels from the model. But, the second optimization strategy dedicated to parsimonious inference turns out to more effective on pure software execution, since it more directly deactivates operations in the convolution process. All in all, this study shows that there is a lot of potential for optimisation and improvement compared to standard deep learning engineering approaches.

#### 5.7. Approach 7 on Confidentiality-Preserving Transfer Learning

- (1)
- How to design a noise adding mechanism that achieves a given differential privacy-loss bound with the minimum loss in accuracy?
- (2)
- How to quantify the privacy-leakage? How to determine the noise model with optimal tradeoff between privacy-leakage and the loss of accuracy?
- (3)
- What is the scope of applicability in terms of assumptions on the distribution of the input data and, what is about model fusion in a transfer learning setting?

**Lesson Learned:**Federated learning offers an infrastructural approach to privacy (and confidentiality, respectively), but further functionalities are required to enhance its privacy-preserving capabilities and scope of applicability. Most important, privacy-preservation of data-driven AI turns out to be a matter of trade-off between privacy-leakage, on the one hand, and loss of accuracy of the target AI model, on the other hand. In this context the concept of differential privacy provides a powerful means of system design. But, the standard design based on a Gaussian noise model is only sub-optimal. The improvement of this trade-off requires refined analysis, as for example, based on exploiting information-theoretic concepts that allow to turn this problem into a feasible optimization problem. However, particularly for industrial settings, when it comes to deviating statistical data characteristics of its sources, respectively, the target application, further research is required to enhance the scope of applicability of privacy-preserving federated learning towards transfer learning.

#### 5.8. Approach 8 on Human AI Teaming as Key to Human Centered AI

**Lesson Learned:**As pointed out in Section 3 there is a substantial gap between current state-of-the-art research of AI systems and the requirements posed by ethical guidelines. Future research will rely much more on machine learning on graph structures. Fast updatable knowledge graphs and related knowledge graph embeddings might be a key towards ethics by design enabling human centered AI.

## 6. Discussion and Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Abbreviations

AI | Artificial Intelligence |

AOW | Architecture Optimization Workbench |

BAPC | Before and After Correction Parameter Comparison |

CMD | Centralized Moment Discrepancy |

DaQL | Data Quality Library |

DL | Deep Learning |

DNN | Deep Neural Networks |

GAN | Generative Adversarial Network |

KG | Knowledge Graph |

MDPI | Multidisciplinary Digital Publishing Institute |

ML | Machine Learning |

NLP | Natural Language Processing |

ONNX | Open Neural Network Exchange |

SNPa | Single-Nucleotide Polymorphism array |

XAI | Explainable AI |

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**Figure 1.**Artificial Intelligence (AI) System Engineering Lifecyle comprised of AI modelling cycle and software system development loop.

**Figure 3.**Architecture of Data Quality Library (DaQL) to Monitor Data Quality [85].

**Figure 4.**Illustration of domain adaptation: adapt learning capabilities from an auxiliary known problem with known labels to a new task with deviating distribution and unknown labels [114].

**Figure 5.**A cell segmentation ensemble approach in combination with Generative Adversarial Network approach (GANs) for multimodal data fusion.

**Figure 6.**Schema of Before and After Correction Parameter Comparison (BAPC) [124]. Left: Reference Model produces prediction ${Y}_{ref}$ by means of parameter ${\vartheta}_{ref}$ due to some conventional parameter identification method; Right: An AI Model is trained on ${\left({X}_{i},{\epsilon}_{i}\right)}_{i}$ to compensate for the residuum of the reference model. The interpretation of the AI Model can be grounded on the meaning of the parameter of the reference model.

**Figure 7.**Automated software documentation for AI system engineering [148].

**Figure 8.**General architecture of the ALOHA software framework for Edge AI taking computational resource constraints at training time into account. Nodes in the upper part of the figure represent the key inputs of the tool flow specified by the users, for details see Reference [151].

**Figure 9.**Design of optimal noise model for tackling the tradeoff between privacy-leakage (in terms of bounded mutual information between private data and perturbed released data) and feature distortion (loss of accuracy); for details see Reference [161].

**Figure 10.**A knowledge-graph approach to enhance vector-based machine learning in order to support human AI teaming by taking context and process knowledge into account. A knowledge graph is used as an intermediate representation of data enriched with static and dynamic context information.

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## Share and Cite

**MDPI and ACS Style**

Fischer, L.; Ehrlinger, L.; Geist, V.; Ramler, R.; Sobiezky, F.; Zellinger, W.; Brunner, D.; Kumar, M.; Moser, B. AI System Engineering—Key Challenges and Lessons Learned. *Mach. Learn. Knowl. Extr.* **2021**, *3*, 56-83.
https://doi.org/10.3390/make3010004

**AMA Style**

Fischer L, Ehrlinger L, Geist V, Ramler R, Sobiezky F, Zellinger W, Brunner D, Kumar M, Moser B. AI System Engineering—Key Challenges and Lessons Learned. *Machine Learning and Knowledge Extraction*. 2021; 3(1):56-83.
https://doi.org/10.3390/make3010004

**Chicago/Turabian Style**

Fischer, Lukas, Lisa Ehrlinger, Verena Geist, Rudolf Ramler, Florian Sobiezky, Werner Zellinger, David Brunner, Mohit Kumar, and Bernhard Moser. 2021. "AI System Engineering—Key Challenges and Lessons Learned" *Machine Learning and Knowledge Extraction* 3, no. 1: 56-83.
https://doi.org/10.3390/make3010004